{ "cells": [ { "cell_type": "markdown", "id": "8dd43ef7", "metadata": {}, "source": [ "# Dataset 101 : Mechanics\n", "\n", "This notebook will make use of lours's data object.\n", "How to load from a known dataset format, how to merge two datasets, how to remap classes, and how to write it on disk in a wanted format" ] }, { "cell_type": "code", "execution_count": 1, "id": "a9865afa", "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "\n", "%autoreload 2\n", "from lours.dataset import Dataset, from_coco\n", "from lours.utils.testing import assert_dataset_equal" ] }, { "cell_type": "markdown", "id": "360b5478", "metadata": {}, "source": [ "Loading coco eval in test folders. Note that you can also load cAIpy and darknet." ] }, { "cell_type": "code", "execution_count": 2, "id": "62a029e4", "metadata": {}, "outputs": [], "source": [ "COCO_dataset = from_coco(\"notebook_data/coco_valid.json\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "0feaba1c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3542af2d452c45d9a2de907af0446c5c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset" ] }, { "cell_type": "markdown", "id": "5823f4e0", "metadata": {}, "source": [ "## Dataset Sampling\n", "\n", "You can use the `loc[]` or `.iloc[]` interface to sample the sub-datasets you want at the image level. To sample at the annotation level, you can use `.loc_annot[]` and `.iloc_annot[]` methods\n", "\n", "Notes:\n", "\n", " - For `iloc`, images indices are not considered, only the row number (like in pandas.DataFrame.iloc), so you might want to reorder the images before, or use `loc` that uses indices\n", " - calling a single number, e.g. `dataset[0]` will give you a dataset of only one image but it will still be a dataset object with two dataframes\n", " - Images are never loaded by the dataset object itself, you need to load them yourself in your pipeline\n", " - the `[]` method is equivalent to `iloc[]`" ] }, { "cell_type": "markdown", "id": "daf90ac6-4927-470e-a7b4-049f71cb2a32", "metadata": {}, "source": [ "### Image based sampling" ] }, { "cell_type": "code", "execution_count": 4, "id": "45882700", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "839b049fb7f1415da488222917572091", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Only taking 50% of the images\n", "COCO_dataset.iloc[::2]" ] }, { "cell_type": "code", "execution_count": 5, "id": "462b60cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index([352582, 113354, 58393, 147729, 310072, 50149, 519208, 356125, 38048,\n", " 567825,\n", " ...\n", " 166478, 185409, 577976, 189806, 363188, 311180, 302030, 105455, 428280,\n", " 349837],\n", " dtype='int64', name='id', length=4722)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7e2618f90e4841b4bb23eed22a262b27", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ids_to_keep = COCO_dataset.images.index[COCO_dataset.images.index > 30_000]\n", "print(ids_to_keep)\n", "COCO_dataset.loc[ids_to_keep]" ] }, { "cell_type": "markdown", "id": "4c46aa67-fce4-4835-801d-e27cc2e14fbb", "metadata": {}, "source": [ "This is equivalent to using `filter_images` method with `loc` mode" ] }, { "cell_type": "code", "execution_count": 6, "id": "241cd185-92ab-4a6e-b033-4c2943c714ed", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ea3aef7e74fd4993a10850c12aa9ef62", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset.filter_images(ids_to_keep, mode=\"loc\")" ] }, { "cell_type": "markdown", "id": "6685d27a-b03b-4e45-9043-c005193f31d2", "metadata": {}, "source": [ "### Annotation based sampling\n", "\n", "Remove half the annotations" ] }, { "cell_type": "code", "execution_count": 7, "id": "8ed8ab68-45a2-46c5-9fae-6406bb5d2f5a", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9c276f07f9a94174b46eb4a8e4ef66e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset.iloc_annot[::2]" ] }, { "cell_type": "markdown", "id": "681152c9-c50e-4fff-b1c8-21139634c1f6", "metadata": {}, "source": [ "Remove half the annotations, remove images emptied of annotations (but keep the ones that were already empty)" ] }, { "cell_type": "code", "execution_count": 8, "id": "91eb6800-f939-46f8-84d8-9c59c0293eec", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fbf95a22080b42069f45fa21cb13bec1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "to_keep = COCO_dataset.annotations.index[::2]\n", "filtered = COCO_dataset.filter_annotations(\n", " to_keep, mode=\"loc\", remove_emptied_images=True\n", ")\n", "display(filtered)" ] }, { "cell_type": "markdown", "id": "6a1531b7-ff3a-4c7a-b111-e48a19b808ff", "metadata": {}, "source": [ "You can also use `slice(None, None, 2)` with the `iloc` mode" ] }, { "cell_type": "code", "execution_count": 9, "id": "4d467f11-4b84-4129-8ac5-6f15850e2770", "metadata": {}, "outputs": [], "source": [ "filtered_2 = COCO_dataset.filter_annotations(\n", " slice(None, None, 2), mode=\"iloc\", remove_emptied_images=True\n", ")\n", "\n", "assert_dataset_equal(filtered, filtered_2)" ] }, { "cell_type": "markdown", "id": "dba6bb16-a44f-4b6d-a7c3-74c81a5d784d", "metadata": {}, "source": [ "### Iterating through the dataset\n", "\n", "You can iterate through the dataset" ] }, { "cell_type": "code", "execution_count": 10, "id": "bec60af8", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1830c6af94d14cc9b2a0f995de05de0e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2765d25940f94154ab972f0a4163552f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for single_image_dataset in COCO_dataset[:2]:\n", " display(single_image_dataset)" ] }, { "cell_type": "markdown", "id": "75c23a7f-8d4f-42d2-8a77-e8d3a04c17cf", "metadata": {}, "source": [ "The `iter_image` method can help you get directly image and annotations dataframes instead of Dataset objects with a single image)\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "6d9b5cda", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "width 425\n", "height 640\n", "relative_path Images/valid/000000352582.jpg\n", "type .jpg\n", "split valid\n", "Name: 352582, dtype: object\n" ] }, { "data": { "text/html": [ "

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image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.6791
535917352582person1valid0.00256.0080.54376.8122650.7380
602093352582frisbee34valid171.63424.0385.8940.672605.7209
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" ], "text/plain": [ " image_id category_str category_id split box_x_min box_y_min \\\n", "id \n", "460450 352582 person 1 valid 112.43 195.32 \n", "535917 352582 person 1 valid 0.00 256.00 \n", "602093 352582 frisbee 34 valid 171.63 424.03 \n", "\n", " box_width box_height area \n", "id \n", "460450 214.78 438.19 48685.6791 \n", "535917 80.54 376.81 22650.7380 \n", "602093 85.89 40.67 2605.7209 " ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "width 640\n", "height 480\n", "relative_path Images/valid/000000113354.jpg\n", "type .jpg\n", "split valid\n", "Name: 113354, dtype: object\n" ] }, { "data": { "text/html": [ "
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image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
592005113354zebra24valid3.24151.28265.34175.8216206.37480
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" ], "text/plain": [ " image_id category_str category_id split box_x_min box_y_min \\\n", "id \n", "589077 113354 zebra 24 valid 260.99 158.88 \n", "589740 113354 zebra 24 valid 366.49 174.59 \n", "592005 113354 zebra 24 valid 3.24 151.28 \n", "\n", " box_width box_height area \n", "id \n", "589077 141.52 194.11 9978.94125 \n", "589740 115.67 142.71 5784.68620 \n", "592005 265.34 175.82 16206.37480 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for image, annotations in COCO_dataset[:2].iter_images():\n", " print(image)\n", " display(annotations)" ] }, { "cell_type": "code", "execution_count": 12, "id": "9aeb39a4-ff36-430d-beed-ec0fca71a189", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "width 425\n", "height 640\n", "relative_path Images/valid/000000352582.jpg\n", "type .jpg\n", "split valid\n", "Name: 352582, dtype: object\n" ] }, { "data": { "text/html": [ "
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image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
592005113354zebra24valid3.24151.28265.34175.8216206.37480
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" ], "text/plain": [ " image_id category_str category_id split box_x_min box_y_min \\\n", "id \n", "589077 113354 zebra 24 valid 260.99 158.88 \n", "589740 113354 zebra 24 valid 366.49 174.59 \n", "592005 113354 zebra 24 valid 3.24 151.28 \n", "\n", " box_width box_height area \n", "id \n", "589077 141.52 194.11 9978.94125 \n", "589740 115.67 142.71 5784.68620 \n", "592005 265.34 175.82 16206.37480 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image, annotation = COCO_dataset[:2].get_one_frame(0)\n", "print(image)\n", "display(annotations)" ] }, { "cell_type": "markdown", "id": "23d1ac33", "metadata": {}, "source": [ "## Remap classes\n", "\n", "Here we use the preset COCO -> Pascal to convert coco classes into Pascal's annotation book" ] }, { "cell_type": "code", "execution_count": 13, "id": "0370d148", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 'person',\n", " 2: 'bicycle',\n", " 3: 'car',\n", " 4: 'motorcycle',\n", " 5: 'airplane',\n", " 6: 'bus',\n", " 7: 'train',\n", " 8: 'truck',\n", " 9: 'boat',\n", " 10: 'traffic light',\n", " 11: 'fire hydrant',\n", " 13: 'stop sign',\n", " 14: 'parking meter',\n", " 15: 'bench',\n", " 16: 'bird',\n", " 17: 'cat',\n", " 18: 'dog',\n", " 19: 'horse',\n", " 20: 'sheep',\n", " 21: 'cow',\n", " 22: 'elephant',\n", " 23: 'bear',\n", " 24: 'zebra',\n", " 25: 'giraffe',\n", " 27: 'backpack',\n", " 28: 'umbrella',\n", " 31: 'handbag',\n", " 32: 'tie',\n", " 33: 'suitcase',\n", " 34: 'frisbee',\n", " 35: 'skis',\n", " 36: 'snowboard',\n", " 37: 'sports ball',\n", " 38: 'kite',\n", " 39: 'baseball bat',\n", " 40: 'baseball glove',\n", " 41: 'skateboard',\n", " 42: 'surfboard',\n", " 43: 'tennis racket',\n", " 44: 'bottle',\n", " 46: 'wine glass',\n", " 47: 'cup',\n", " 48: 'fork',\n", " 49: 'knife',\n", " 50: 'spoon',\n", " 51: 'bowl',\n", " 52: 'banana',\n", " 53: 'apple',\n", " 54: 'sandwich',\n", " 55: 'orange',\n", " 56: 'broccoli',\n", " 57: 'carrot',\n", " 58: 'hot dog',\n", " 59: 'pizza',\n", " 60: 'donut',\n", " 61: 'cake',\n", " 62: 'chair',\n", " 63: 'couch',\n", " 64: 'potted plant',\n", " 65: 'bed',\n", " 67: 'dining table',\n", " 70: 'toilet',\n", " 72: 'tv',\n", " 73: 'laptop',\n", " 74: 'mouse',\n", " 75: 'remote',\n", " 76: 'keyboard',\n", " 77: 'cell phone',\n", " 78: 'microwave',\n", " 79: 'oven',\n", " 80: 'toaster',\n", " 81: 'sink',\n", " 82: 'refrigerator',\n", " 84: 'book',\n", " 85: 'clock',\n", " 86: 'vase',\n", " 87: 'scissors',\n", " 88: 'teddy bear',\n", " 89: 'hair drier',\n", " 90: 'toothbrush'}" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "COCO_dataset.label_map" ] }, { "cell_type": "code", "execution_count": 14, "id": "f98b9239", "metadata": {}, "outputs": [], "source": [ "COCO_pascal = COCO_dataset.remap_from_preset(\"coco\", \"pascalvoc\")" ] }, { "cell_type": "markdown", "id": "ee0ae0f8", "metadata": {}, "source": [ "See how label map tab has changed" ] }, { "cell_type": "code", "execution_count": 15, "id": "13ccea81", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "894942382e4348e19192c4e35fff114f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_pascal" ] }, { "cell_type": "markdown", "id": "75af882b", "metadata": {}, "source": [ "### Remap from dictionaries\n", "\n", "Fictional usecase where we want to only have vehicles, bags and animals.\n", "If given, new_names must be the length of distinct values in class_mapping" ] }, { "cell_type": "code", "execution_count": 16, "id": "f1d57135", "metadata": {}, "outputs": [], "source": [ "COCO_RT = COCO_pascal.remap_classes(\n", " class_mapping={\n", " 1: 2,\n", " 2: 2,\n", " 3: 1,\n", " 4: 1,\n", " 5: 3,\n", " 6: 2,\n", " 7: 2,\n", " 8: 1,\n", " 9: 3,\n", " 10: 1,\n", " 11: 3,\n", " 12: 1,\n", " 13: 1,\n", " 14: 2,\n", " 16: 3,\n", " 17: 1,\n", " 18: 3,\n", " 19: 2,\n", " 20: 3,\n", " },\n", " new_names={1: \"Animal\", 2: \"Vehicle\", 3: \"Object\"},\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "id": "132bce91", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "46848b28dac8460abe51743163af9939", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_RT" ] }, { "cell_type": "markdown", "id": "0c615950", "metadata": {}, "source": [ "### Remap from dataframe\n", "\n", "Dataframe for remapping must have at least 2 columns : `input_category_id` and `output_category_id`\n", "\n", "If available, `output_category_name` will be use to replace the names of remapped ids.\n", "\n", "`input_category_name` only serves an informative purpose." ] }, { "cell_type": "code", "execution_count": 18, "id": "6afdf8fc", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "class_table = (\n", " pd.Series(COCO_pascal.label_map).rename(\"input_category_name\").sort_index()\n", ")\n", "class_table.index.rename(\"input_category_id\", inplace=True)\n", "class_table = class_table.reset_index().drop(15)\n", "class_table[\"output_category_id\"] = [\n", " 2,\n", " 2,\n", " 1,\n", " 2,\n", " 3,\n", " 2,\n", " 2,\n", " 1,\n", " 3,\n", " 1,\n", " 3,\n", " 1,\n", " 1,\n", " 2,\n", " 3,\n", " 1,\n", " 3,\n", " 2,\n", " 3,\n", "]\n", "class_table[\"output_category_name\"] = class_table[\"output_category_id\"].replace(\n", " {1: \"animal\", 2: \"vehicle\", 3: \"object\"}\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "id": "43e1a9eb", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "

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input_category_idinput_category_nameoutput_category_idoutput_category_name
01aeroplane2vehicle
12bicycle2vehicle
23bird1animal
34boat2vehicle
45bottle3object
56bus2vehicle
67car2vehicle
78cat1animal
89chair3object
910cow1animal
1011diningtable3object
1112dog1animal
1213horse1animal
1314motorbike2vehicle
1415person3object
1617sheep1animal
1718sofa3object
1819train2vehicle
1920tvmonitor3object
\n", "
" ], "text/plain": [ " input_category_id input_category_name output_category_id \\\n", "0 1 aeroplane 2 \n", "1 2 bicycle 2 \n", "2 3 bird 1 \n", "3 4 boat 2 \n", "4 5 bottle 3 \n", "5 6 bus 2 \n", "6 7 car 2 \n", "7 8 cat 1 \n", "8 9 chair 3 \n", "9 10 cow 1 \n", "10 11 diningtable 3 \n", "11 12 dog 1 \n", "12 13 horse 1 \n", "13 14 motorbike 2 \n", "14 15 person 3 \n", "16 17 sheep 1 \n", "17 18 sofa 3 \n", "18 19 train 2 \n", "19 20 tvmonitor 3 \n", "\n", " output_category_name \n", "0 vehicle \n", "1 vehicle \n", "2 animal \n", "3 vehicle \n", "4 object \n", "5 vehicle \n", "6 vehicle \n", "7 animal \n", "8 object \n", "9 animal \n", "10 object \n", "11 animal \n", "12 animal \n", "13 vehicle \n", "14 object \n", "16 animal \n", "17 object \n", "18 vehicle \n", "19 object " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_table" ] }, { "cell_type": "code", "execution_count": 20, "id": "bd8e9292", "metadata": {}, "outputs": [], "source": [ "COCO_RT_DF = COCO_pascal.remap_from_dataframe(class_table)" ] }, { "cell_type": "code", "execution_count": 21, "id": "5adf94a8", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a9f2687fd4be4fb5b68c5c24c5dcca30", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_RT_DF" ] }, { "cell_type": "markdown", "id": "c4e2d597", "metadata": {}, "source": [ "### Remap from CSV\n", "\n", "Basically the same as remap from dataframe, except the input is a csv file with the same data" ] }, { "cell_type": "code", "execution_count": 22, "id": "5576ee64", "metadata": {}, "outputs": [], "source": [ "csv_file = \"remap.csv\"\n", "class_table.to_csv(csv_file, index=False)" ] }, { "cell_type": "code", "execution_count": 23, "id": "43c67237", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "input_category_id,input_category_name,output_category_id,output_category_name\n", "1,aeroplane,2,vehicle\n", "2,bicycle,2,vehicle\n", "3,bird,1,animal\n", "4,boat,2,vehicle\n", "5,bottle,3,object\n", "6,bus,2,vehicle\n", "7,car,2,vehicle\n", "8,cat,1,animal\n", "9,chair,3,object\n", "10,cow,1,animal\n", "11,diningtable,3,object\n", "12,dog,1,animal\n", "13,horse,1,animal\n", "14,motorbike,2,vehicle\n", "15,person,3,object\n", "17,sheep,1,animal\n", "18,sofa,3,object\n", "19,train,2,vehicle\n", "20,tvmonitor,3,object\n" ] } ], "source": [ "!cat remap.csv" ] }, { "cell_type": "code", "execution_count": 24, "id": "6e64cee3", "metadata": {}, "outputs": [], "source": [ "COCO_RT_CSV = COCO_pascal.remap_from_csv(csv_file)" ] }, { "cell_type": "code", "execution_count": 25, "id": "661820a1", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0324997a024e48a282a0acc8c897a940", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_RT_CSV" ] }, { "cell_type": "markdown", "id": "6ad1ee14-9f78-49e7-b3f9-a66e62f586e8", "metadata": {}, "source": [ "### Remap from other dataset\n", "\n", "This method will try to retrieve the label names in the other dataset and apply a remapping accordingly.\n", "\n", "classes that are not in the other dataset are mapped to a free id with respect to the other dataset's label map." ] }, { "cell_type": "code", "execution_count": 26, "id": "ca0b35e5-5424-4f0b-a6e0-d08f00a34ab7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using the following class remapping dictionary :\n", "{1: 22,\n", " 2: 21,\n", " 3: 23,\n", " 4: 4,\n", " 5: 5,\n", " 6: 6,\n", " 7: 7,\n", " 8: 8,\n", " 9: 9,\n", " 10: 10,\n", " 11: 11,\n", " 12: 12,\n", " 13: 13,\n", " 14: 14,\n", " 15: 15,\n", " 16: 16,\n", " 17: 17,\n", " 18: 18,\n", " 19: 19,\n", " 20: 20}\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7818182c65624d0f8d94471d24684f68", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_RT_other = COCO_pascal.remap_from_other(COCO_RT_CSV)\n", "COCO_RT_CSV" ] }, { "cell_type": "markdown", "id": "eb84deb8-c5ee-4f0c-9581-f20b883d16b8", "metadata": {}, "source": [ "## Dataset Reindexing\n", "\n", "### Resetting index\n", "\n", "The `reset_index` method allows you to reorder the dataset's dataframes according to some column values" ] }, { "cell_type": "code", "execution_count": 27, "id": "36b551b2-ff99-4d2f-835b-2d1836224770", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6b694a522a394ca9b67905fac307a804", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset.reset_index()" ] }, { "cell_type": "markdown", "id": "79cd558b-a37b-45d9-bf3e-59b1857d2a19", "metadata": {}, "source": [ "Sort the annotations by category string first : Get the dataframe to start with airplanes and finish with zebra." ] }, { "cell_type": "code", "execution_count": 28, "id": "77c46450-874b-4ddc-a926-367b4e3fd4db", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0b06b68be3734a76b0db0808865c20f6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "reset_COCO_dataset = COCO_dataset.reset_index(\n", " start_image_id=10,\n", " start_annotations_id=2,\n", " sort_annotations_by=(\"category_str\", \"image_id\"),\n", ")\n", "reset_COCO_dataset" ] }, { "cell_type": "markdown", "id": "b910f594-11f5-450d-9713-464118823637", "metadata": {}, "source": [ "### Reindex with mapping\n", "\n", "Akin to class remapping, you can also remap the dataset's dataframe indexes with dictionaries. Note that unmapped index values will be reset to a range index, but they will not be sorted. Be sure to sort the dataframes the way you want before calling the method `reset_index_from_mapping` with an incomplete index mapping." ] }, { "cell_type": "code", "execution_count": 29, "id": "6e585ae1-5a68-40b1-81e5-1336b56bf0f5", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "87fabb6909cd4796a02287f7620b30bd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset.reset_index_from_mapping(\n", " images_index_map={58393: 0}, annotations_index_map={331107: 0}\n", ")" ] }, { "cell_type": "markdown", "id": "1f9d3ba0-863c-4992-aa4e-a61acd7de77d", "metadata": {}, "source": [ "### Reindex images index from other dataframe\n", "\n", "This feature is similar to panda's [merge](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html) function : by selecting columns to merge on, the dataset will construct an index mapping for entries that are in both original images dataframe and the other dataframe, and optionally remap the other rows to a simple range index" ] }, { "cell_type": "code", "execution_count": 30, "id": "5c7c3b19-faf8-4133-9361-d1859ca131c0", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9cee0d099ede47c38f043758df377b04", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "

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widthheightrelative_pathtypesplit
id
10640426Images/valid/000000000139.jpg.jpgvalid
11586640Images/valid/000000000285.jpg.jpgvalid
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13375500Images/valid/000000000724.jpg.jpgvalid
14428640Images/valid/000000000776.jpg.jpgvalid
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5005640354Images/valid/000000581317.jpg.jpgvalid
5006612612Images/valid/000000581357.jpg.jpgvalid
5007640427Images/valid/000000581482.jpg.jpgvalid
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5000 rows × 5 columns

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" ], "text/plain": [ " width height relative_path type split\n", "id \n", "10 640 426 Images/valid/000000000139.jpg .jpg valid\n", "11 586 640 Images/valid/000000000285.jpg .jpg valid\n", "12 640 483 Images/valid/000000000632.jpg .jpg valid\n", "13 375 500 Images/valid/000000000724.jpg .jpg valid\n", "14 428 640 Images/valid/000000000776.jpg .jpg valid\n", "... ... ... ... ... ...\n", "5005 640 354 Images/valid/000000581317.jpg .jpg valid\n", "5006 612 612 Images/valid/000000581357.jpg .jpg valid\n", "5007 640 427 Images/valid/000000581482.jpg .jpg valid\n", "5008 478 640 Images/valid/000000581615.jpg .jpg valid\n", "5009 640 478 Images/valid/000000581781.jpg .jpg valid\n", "\n", "[5000 rows x 5 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matched_COCO = COCO_dataset.match_index(reset_COCO_dataset.images, on=\"relative_path\")\n", "display(matched_COCO)\n", "\n", "matched_COCO.images.sort_index()" ] }, { "cell_type": "markdown", "id": "11316769", "metadata": {}, "source": [ "## Dataset merge\n", "\n", "### Regular merge\n", "\n", "Here, we divide COCO in two and merge them again to show how it works" ] }, { "cell_type": "code", "execution_count": 31, "id": "fad3507e", "metadata": {}, "outputs": [], "source": [ "half1 = COCO_dataset[::2]\n", "half2 = COCO_dataset[1::2]" ] }, { "cell_type": "code", "execution_count": 32, "id": "47348dd6", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3705bba3da4b4d8888ddf8632a4ef809", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5c42c424c9dd4c08bd87e0f7e307e3fe", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lours.utils.testing import assert_dataset_equal\n", "\n", "merged_back = half1 + half2\n", "display(merged_back)\n", "display(COCO_dataset)\n", "assert_dataset_equal(COCO_dataset, merged_back)" ] }, { "cell_type": "markdown", "id": "16546503", "metadata": {}, "source": [ "### Merge with `ignore_index`\n", "\n", "the merge function can be used with `ignore_index` when image ids are overlapping" ] }, { "cell_type": "code", "execution_count": 33, "id": "47cb8ffb", "metadata": {}, "outputs": [], "source": [ "half1 = half1.reset_index()\n", "half2 = half2.reset_index()" ] }, { "cell_type": "code", "execution_count": 34, "id": "2a1f28f6", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "276b9e6a229d40f1bf0e2dfbe6aa5394", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "merged_back = half1.merge(half2, ignore_index=True)\n", "assert_dataset_equal(merged_back, COCO_dataset, ignore_index=True)\n", "merged_back" ] }, { "cell_type": "markdown", "id": "e749611b", "metadata": {}, "source": [ "### Merging with overlapping ids\n", "\n", "If your datasets have images with overlapping ids, they can still be merged as long as the overlapping subset are the exact same" ] }, { "cell_type": "code", "execution_count": 35, "id": "cb2350a1", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "df16b3e64151406f8c3c36f3c8b766ff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "56b97a3e4d4745c9941c7551b6d5922d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "half1 = Dataset.from_template(\n", " COCO_dataset, annotations=COCO_dataset.annotations.iloc[::2]\n", ")\n", "display(half1)\n", "half2 = Dataset.from_template(\n", " COCO_dataset, annotations=COCO_dataset.annotations.iloc[1::2]\n", ")\n", "display(half2)\n", "merged_back = half1 + half2\n", "assert_dataset_equal(COCO_dataset, merged_back)" ] }, { "cell_type": "markdown", "id": "16ebfbd3", "metadata": {}, "source": [ "Merging overlapping ids can be turned off with `allow_overlapping_ids` set to False." ] }, { "cell_type": "code", "execution_count": 36, "id": "5a165442", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "raises-exception" ] }, "outputs": [ { "ename": "ValueError", "evalue": "Overlapping image ids not permitted. Consider using the allow_overlapping_image_ids or ignore_index options", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mhalf1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhalf2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_overlapping_image_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/workspace/Bamboo/lours/dataset/dataset.py:2803\u001b[0m, in \u001b[0;36mDataset.merge\u001b[0;34m(self, other, allow_overlapping_image_ids, realign_label_map, ignore_index, mark_origin, overwrite_origin)\u001b[0m\n\u001b[1;32m 2344\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Merge two datasets and return a unique dataset object containing\u001b[39;00m\n\u001b[1;32m 2345\u001b[0m \u001b[38;5;124;03mSamples from both. Result's images_root will be the common path of both\u001b[39;00m\n\u001b[1;32m 2346\u001b[0m \u001b[38;5;124;03mdatasets, and the image relative paths will be updated accordingly.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2799\u001b[0m \n\u001b[1;32m 2800\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2801\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmerge\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m merge_datasets\n\u001b[0;32m-> 2803\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmerge_datasets\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2804\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2805\u001b[0m \u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2806\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_overlapping_image_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_overlapping_image_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2807\u001b[0m \u001b[43m \u001b[49m\u001b[43mrealign_label_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrealign_label_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2808\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2809\u001b[0m \u001b[43m \u001b[49m\u001b[43mmark_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmark_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2810\u001b[0m \u001b[43m \u001b[49m\u001b[43moverwrite_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2811\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/workspace/Bamboo/lours/dataset/merge.py:167\u001b[0m, in \u001b[0;36mmerge_datasets\u001b[0;34m(dataset1, dataset2, allow_overlapping_image_ids, realign_label_map, ignore_index, mark_origin, overwrite_origin)\u001b[0m\n\u001b[1;32m 164\u001b[0m mutual_images_columns \u001b[38;5;241m=\u001b[39m dataset1_images_columns \u001b[38;5;241m&\u001b[39m dataset2_images_columns\n\u001b[1;32m 166\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mutual_images_ids \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m allow_overlapping_image_ids:\n\u001b[0;32m--> 167\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 168\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOverlapping image ids not permitted. Consider using the\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 169\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m allow_overlapping_image_ids or ignore_index options\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 170\u001b[0m )\n\u001b[1;32m 172\u001b[0m assert_frame_intersections_equal(\n\u001b[1;32m 173\u001b[0m dataset1_images\u001b[38;5;241m.\u001b[39mdrop([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morigin_id\u001b[39m\u001b[38;5;124m\"\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 174\u001b[0m dataset2_images\u001b[38;5;241m.\u001b[39mdrop([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morigin\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morigin_id\u001b[39m\u001b[38;5;124m\"\u001b[39m], axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[1;32m 175\u001b[0m )\n\u001b[1;32m 177\u001b[0m \u001b[38;5;66;03m# Concat horizontally by extending images from dataset1 with columns from dataset2\u001b[39;00m\n\u001b[1;32m 178\u001b[0m \u001b[38;5;66;03m# and then vertically by extending images with dataset2 images which id is not\u001b[39;00m\n\u001b[1;32m 179\u001b[0m \u001b[38;5;66;03m# in dataset1 images index.\u001b[39;00m\n", "\u001b[0;31mValueError\u001b[0m: Overlapping image ids not permitted. Consider using the allow_overlapping_image_ids or ignore_index options" ] } ], "source": [ "half1.merge(half2, allow_overlapping_image_ids=False)" ] }, { "cell_type": "markdown", "id": "61cfd434", "metadata": {}, "source": [ "### Incompatible Label maps\n", "\n", "In the case the label map of one dataset is not the subset of the other and vice versa, the label maps are incompatible." ] }, { "cell_type": "code", "execution_count": 37, "id": "19adeab2", "metadata": {}, "outputs": [], "source": [ "new_label_map = {**COCO_pascal.label_map, **{1: \"something else\"}}\n", "COCO_incompatible = COCO_pascal.from_template(label_map=new_label_map)" ] }, { "cell_type": "code", "execution_count": 38, "id": "f24744b4-8ab4-4e8d-86eb-7bf53e865fb9", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [ "raises-exception" ] }, "outputs": [ { "ename": "IncompatibleLabelMapsError", "evalue": "Label maps are incompatible", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIncompatibleLabelMapsError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mCOCO_pascal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmerge\u001b[49m\u001b[43m(\u001b[49m\u001b[43mCOCO_incompatible\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/workspace/Bamboo/lours/dataset/dataset.py:2803\u001b[0m, in \u001b[0;36mDataset.merge\u001b[0;34m(self, other, allow_overlapping_image_ids, realign_label_map, ignore_index, mark_origin, overwrite_origin)\u001b[0m\n\u001b[1;32m 2344\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Merge two datasets and return a unique dataset object containing\u001b[39;00m\n\u001b[1;32m 2345\u001b[0m \u001b[38;5;124;03mSamples from both. Result's images_root will be the common path of both\u001b[39;00m\n\u001b[1;32m 2346\u001b[0m \u001b[38;5;124;03mdatasets, and the image relative paths will be updated accordingly.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2799\u001b[0m \n\u001b[1;32m 2800\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2801\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmerge\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m merge_datasets\n\u001b[0;32m-> 2803\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmerge_datasets\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2804\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2805\u001b[0m \u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2806\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_overlapping_image_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_overlapping_image_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2807\u001b[0m \u001b[43m \u001b[49m\u001b[43mrealign_label_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrealign_label_map\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2808\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2809\u001b[0m \u001b[43m \u001b[49m\u001b[43mmark_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmark_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2810\u001b[0m \u001b[43m \u001b[49m\u001b[43moverwrite_origin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moverwrite_origin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2811\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/workspace/Bamboo/lours/dataset/merge.py:138\u001b[0m, in \u001b[0;36mmerge_datasets\u001b[0;34m(dataset1, dataset2, allow_overlapping_image_ids, realign_label_map, ignore_index, mark_origin, overwrite_origin)\u001b[0m\n\u001b[1;32m 134\u001b[0m label_map \u001b[38;5;241m=\u001b[39m merge_label_maps(\n\u001b[1;32m 135\u001b[0m dataset1\u001b[38;5;241m.\u001b[39mlabel_map, dataset2\u001b[38;5;241m.\u001b[39mlabel_map, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mouter\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 136\u001b[0m )\n\u001b[1;32m 137\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 138\u001b[0m label_map \u001b[38;5;241m=\u001b[39m \u001b[43mmerge_label_maps\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 139\u001b[0m \u001b[43m \u001b[49m\u001b[43mdataset1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mouter\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[1;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 142\u001b[0m dataset1_images, dataset2_images, booleanized_image_columns \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 143\u001b[0m broadcast_booleanization(\n\u001b[1;32m 144\u001b[0m dataset1\u001b[38;5;241m.\u001b[39mimages,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 148\u001b[0m )\n\u001b[1;32m 149\u001b[0m )\n\u001b[1;32m 150\u001b[0m dataset1_annotations, dataset2_annotations, booleanized_annotations_columns \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 151\u001b[0m broadcast_booleanization(\n\u001b[1;32m 152\u001b[0m dataset1\u001b[38;5;241m.\u001b[39mannotations,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 156\u001b[0m )\n\u001b[1;32m 157\u001b[0m )\n", "File \u001b[0;32m~/workspace/Bamboo/lours/utils/label_map_merger.py:66\u001b[0m, in \u001b[0;36mmerge_label_maps\u001b[0;34m(left, right, method)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;66;03m# The other way around when the other dataset's label map is the biggest\u001b[39;00m\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m {k: left[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m intersection} \u001b[38;5;241m!=\u001b[39m {k: right[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m intersection}:\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IncompatibleLabelMapsError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLabel maps are incompatible\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 67\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m left \u001b[38;5;241m|\u001b[39m right\n\u001b[1;32m 68\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", "\u001b[0;31mIncompatibleLabelMapsError\u001b[0m: Label maps are incompatible" ] } ], "source": [ "COCO_pascal.merge(COCO_incompatible)" ] }, { "cell_type": "markdown", "id": "f844b99d", "metadata": {}, "source": [ "If we lookup the label map of SmartCity, we can see that class labels are not the same for class id 41 (dog vs domestic animal)" ] }, { "cell_type": "code", "execution_count": 39, "id": "4e6faca3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Incompatible label map for category_id 1 : 'aeroplane' vs 'something else'\n" ] } ], "source": [ "for k, name in COCO_pascal.label_map.items():\n", " other_name = COCO_incompatible.label_map.get(k)\n", " if other_name is not None and other_name != name:\n", " print(\n", " f\"Incompatible label map for category_id {k} : '{name}' vs '{other_name}'\"\n", " )" ] }, { "cell_type": "markdown", "id": "4f944206-fb1a-45ce-80c3-a9c5ac182ece", "metadata": {}, "source": [ "### Automatic remapping\n", "\n", "It is possible though to remap a dataset to match another dataset's label map by retrieving categories with the same names.\n", "\n", "We can use either the `remap_from_other` method or directly use the addition as it will fallback to the automatic remapping with a warning.\n", "\n", "Note that the merge is effective but you should avoid this fallback mechanism if possible, because label names are not supposed to be used as ids." ] }, { "cell_type": "code", "execution_count": 40, "id": "2ad07471", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using the following class remapping dictionary :\n", "{1: 21,\n", " 2: 2,\n", " 3: 3,\n", " 4: 4,\n", " 5: 5,\n", " 6: 6,\n", " 7: 7,\n", " 8: 8,\n", " 9: 9,\n", " 10: 10,\n", " 11: 11,\n", " 12: 12,\n", " 13: 13,\n", " 14: 14,\n", " 15: 15,\n", " 16: 16,\n", " 17: 17,\n", " 18: 18,\n", " 19: 19,\n", " 20: 20}\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c1120547b03e442abc27bc1dd3d922d4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "remapped = COCO_incompatible.remap_from_other(COCO_pascal)\n", "merged = COCO_pascal.merge(remapped)\n", "merged" ] }, { "cell_type": "code", "execution_count": 41, "id": "192cf38a-f5ab-40e8-bb79-412c56ca7e58", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using the following class remapping dictionary :\n", "{1: 21,\n", " 2: 2,\n", " 3: 3,\n", " 4: 4,\n", " 5: 5,\n", " 6: 6,\n", " 7: 7,\n", " 8: 8,\n", " 9: 9,\n", " 10: 10,\n", " 11: 11,\n", " 12: 12,\n", " 13: 13,\n", " 14: 14,\n", " 15: 15,\n", " 16: 16,\n", " 17: 17,\n", " 18: 18,\n", " 19: 19,\n", " 20: 20}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/clement.pinard/workspace/Bamboo/lours/dataset/dataset.py:2843: RuntimeWarning: Addition failed because of incompatible label maps, trying to remap classes of right value and retry the merge\n", " warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "891e741c61ef4eee9c8edaf8db76710b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "merged = COCO_incompatible + COCO_pascal\n", "merged" ] }, { "cell_type": "markdown", "id": "187e2f27-49ee-445e-bae0-80c0a8af1ed4", "metadata": {}, "source": [ "## Adding annotations to dataset\n", "\n", "### Standalone annotation addition\n", "\n", "Similar to [pandas.DataFrame.append](https://pandas.pydata.org/pandas-docs/version/1.4/reference/api/pandas.DataFrame.append.html), you can append one annotation row to your annotations dataframe.\n", "\n", "Notice the `box_format` option which will let the method take care of the conversion itself. See [lours.utils.bbox_converter](../generated/lours.utils.bbox_converter.rst) for name conventions. For example yolo bboxes are giving box center x and y coordinates plus box height and width, all normalized with frame size. The format is thus `cxcywh`.\n", "\n", "First, create a dataset with 2 images and no annotation" ] }, { "cell_type": "code", "execution_count": 42, "id": "1188a06e-b468-4733-9da7-d46e2a4e5885", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8ff53f529b73486a9ef4acaf1c15d27f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "empty = COCO_pascal.loc_annot[[]].iloc[:2]\n", "display(empty)" ] }, { "cell_type": "markdown", "id": "b416871a-ae6f-42ae-b2cc-9907f688dae3", "metadata": {}, "source": [ "Here, we add one bounding box, for the first image. the box is a quarter of the image (half the height and half the width) and is at the top-left corner of the image." ] }, { "cell_type": "code", "execution_count": 43, "id": "caade903-3cf9-4cd9-b11d-67c9b028eaa8", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e1170f526454fa09c7b74e7eb8774df", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "empty.add_detection_annotation(\n", " format_string=\"cxcywh\",\n", " image_id=352582,\n", " bbox_coordinates=[0.75, 0.75, 0.5, 0.5],\n", " confidence=0.5,\n", " category_id=20,\n", ")" ] }, { "cell_type": "markdown", "id": "72f9191a-8909-4029-a8f8-412cae67f1c5", "metadata": {}, "source": [ "### Introduction the AnnotationAppender context manager\n", "\n", "Similarly to [pandas.DataFrame.append](https://pandas.pydata.org/pandas-docs/version/1.4/reference/api/pandas.DataFrame.append.html), calling this method multiple times is discouraged, because each time it creates a new dataframe with only one more row.\n", "\n", "What you can do instead is use the `annotation_append` method with a context manager. This appender will cache all the added annotation and will only append the consolidated data when exiting the context.\n", "\n", "This is very useful when running an inference on a whole dataset.\n", "\n", "Note that this operation is inplace !" ] }, { "cell_type": "code", "execution_count": 44, "id": "5d2b9fe1-0f1e-4ac3-8bb5-bd82a07ae7a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/clement.pinard/workspace/Bamboo/lours/dataset/dataset.py:1004: UserWarning: Incomplete Label map, setting following label of the following id to their string equivalent : {21}\n", " warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8434e5f7d00a4bd8a08920774d7cb8d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with empty.annotation_append(format_string=\"cxcywh\") as appender:\n", " appender.append(\n", " image_id=352582,\n", " bbox_coordinates=[0.75, 0.75, 0.5, 0.5],\n", " confidence=0.5,\n", " category_id=20,\n", " )\n", " appender.append(\n", " image_id=113354,\n", " bbox_coordinates=[0.25, 0.25, 0.5, 0.5],\n", " confidence=0.5,\n", " category_id=21,\n", " )\n", " print(empty.len_annot()) # Note that the dataset is not changed here\n", "\n", "display(empty)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "003973ba6429441696690892068b30b5": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_0f141228b6734487b226c5525b127ab0", "outputs": [ { "data": { "text/html": "

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category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
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80 rows × 1 columns

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category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
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80 rows × 1 columns

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image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
535917352582person1valid0.00256.0080.54376.8122650.73800
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
..............................
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
332788349837refrigerator82valid138.0062.6398.25252.0023037.46875
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333685349837refrigerator82valid335.0410.48125.17318.7837187.97840
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

34818 rows × 9 columns

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", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589077 113354 zebra 24 valid 260.99 158.88 \n589740 113354 zebra 24 valid 366.49 174.59 \n... ... ... ... ... ... ... \n331107 349837 refrigerator 82 valid 66.00 94.87 \n332788 349837 refrigerator 82 valid 138.00 62.63 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333685 349837 refrigerator 82 valid 335.04 10.48 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n589077 141.52 194.11 9978.94125 \n589740 115.67 142.71 5784.68620 \n... ... ... ... \n331107 71.25 194.26 12029.44125 \n332788 98.25 252.00 23037.46875 \n333394 113.49 298.65 30406.51295 \n333685 125.17 318.78 37187.97840 \n333731 39.61 328.64 12492.52165 \n\n[34818 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "0324997a024e48a282a0acc8c897a940": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_5bfb24a8b50e45cf952978da02f50a3e", "IPY_MODEL_4f24b4c99bbd4bb986f611bdc3cb0cc5" ], "layout": "IPY_MODEL_9d15ec8503094cc2b2668aaeacb22cb8" } }, "03612feab58e4d9aabc7ddeebd96c2d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "03a9542f0e794f079aad4ea10017013f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "0950d27d57f14519bb7d47741f951050": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0a41d889cee04896b371be88fb621959": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0b06b68be3734a76b0db0808865c20f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_8a0098265e3d4d468308cb112570d1eb", "IPY_MODEL_35c08fe2ab8f4a2ab98fc4dbc7902325" ], "layout": "IPY_MODEL_887db8657dbf4b9182d34277082dfbeb" } }, "0c28b3bd2999413286338ace0fc5d4f7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_53b65b08631e4456a44b729be6620421", "style": "IPY_MODEL_5bfc0fd678c2438e992a5d4eca371e60", "value": "

Dataset object containing 5,000 images and 9,946 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

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Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

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Dataset object containing 5,000 images and 41,900 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

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Dataset object containing 2 images and 1 object\nName :\n\tcoco\nImages root :\n\tnotebook_data

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widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
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5000 rows × 5 columns

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image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
535917352582person1valid0.00256.0080.54376.8122650.73800
589077113354zebra24valid260.99158.88141.52194.119978.94125
592005113354zebra24valid3.24151.28265.34175.8216206.37480
57690058393bench15valid44.78242.27547.16224.9882291.54995
322417147729cell phone77valid254.48117.5449.9275.001306.79705
..............................
578099428280bench15valid0.75184.42181.43132.9013841.81490
1117514428280keyboard76valid121.04165.6540.276.02217.08440
330925349837refrigerator82valid14.97104.0262.11166.128470.60700
332788349837refrigerator82valid138.0062.6398.25252.0023037.46875
333685349837refrigerator82valid335.0410.48125.17318.7837187.97840
\n

18390 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n535917 352582 person 1 valid 0.00 256.00 \n589077 113354 zebra 24 valid 260.99 158.88 \n592005 113354 zebra 24 valid 3.24 151.28 \n576900 58393 bench 15 valid 44.78 242.27 \n322417 147729 cell phone 77 valid 254.48 117.54 \n... ... ... ... ... ... ... \n578099 428280 bench 15 valid 0.75 184.42 \n1117514 428280 keyboard 76 valid 121.04 165.65 \n330925 349837 refrigerator 82 valid 14.97 104.02 \n332788 349837 refrigerator 82 valid 138.00 62.63 \n333685 349837 refrigerator 82 valid 335.04 10.48 \n\n box_width box_height area \nid \n535917 80.54 376.81 22650.73800 \n589077 141.52 194.11 9978.94125 \n592005 265.34 175.82 16206.37480 \n576900 547.16 224.98 82291.54995 \n322417 49.92 75.00 1306.79705 \n... ... ... ... \n578099 181.43 132.90 13841.81490 \n1117514 40.27 6.02 217.08440 \n330925 62.11 166.12 8470.60700 \n332788 98.25 252.00 23037.46875 \n333685 125.17 318.78 37187.97840 \n\n[18390 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "14b9ce2ddbb348d9ae3fd5dab884ad14": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "154a1b375375452988f208e85549c375": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f2c0045fe58b43f59f4729a0edddcbdd", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1animal
2vehicle
3object
\n
", "text/plain": " category string\ncategory_id \n1 animal\n2 vehicle\n3 object" }, "metadata": {}, "output_type": "display_data" } ] } }, "160d21b7b91544a69bd0ec984366f70e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "164a53399de6479db15055e945ecbdd1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "16504d84d20a4054ac3cecaf37bde11d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1830c6af94d14cc9b2a0f995de05de0e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_ea8cea5708c2485c9e3df8088c556f5c", "IPY_MODEL_48d1ff71f56c4763a7ac4d3736fbddf1" ], "layout": "IPY_MODEL_afb1b69762974288866c8d07a7ccc0cb" } }, "196967d5069f4d189f12628fb951211b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1a085fad85ae473fbbcfce695d050dc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1ab75e49bbbc4821a82133fbe61f062e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1b66cc1c194b4bb1b4de420e94dd4e1a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1ca7cc3b062b49f99c27a62e8fda36e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_aab9806ceeab454f9401385c09ef3e1a", "IPY_MODEL_9bd2a2ec87cd4d1cbe44151178f04c26", "IPY_MODEL_dabce91a4a4b454dabeb9c3a486c0d43" ], "layout": "IPY_MODEL_4c5b28016ec346c8978de6c783d973ae", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "1cf069555e2548a9919cb3015944892c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_e82f91fa62f44f708868b2c85acde7b1", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582object3valid112.43195.32214.78438.1948685.67910
535917352582object3valid0.00256.0080.54376.8122650.73800
46790558393object3valid342.52163.33192.2477.475408.64720
171099058393object3valid418.99182.6561.1245.001792.80770
1238519147729object3valid0.0087.01310.67287.9955847.52705
..............................
1192747105455vehicle2valid333.56517.2117.4314.58182.21680
108177428280object3valid3.58189.14176.88125.329942.25095
108343428280object3valid244.05152.2968.5134.491457.96785
109094428280object3valid187.71114.8044.0766.412069.93800
2190513428280object3valid203.58179.65105.27139.557696.12525
\n

20607 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 object 3 valid 112.43 195.32 \n535917 352582 object 3 valid 0.00 256.00 \n467905 58393 object 3 valid 342.52 163.33 \n1710990 58393 object 3 valid 418.99 182.65 \n1238519 147729 object 3 valid 0.00 87.01 \n... ... ... ... ... ... ... \n1192747 105455 vehicle 2 valid 333.56 517.21 \n108177 428280 object 3 valid 3.58 189.14 \n108343 428280 object 3 valid 244.05 152.29 \n109094 428280 object 3 valid 187.71 114.80 \n2190513 428280 object 3 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n1238519 310.67 287.99 55847.52705 \n... ... ... ... \n1192747 17.43 14.58 182.21680 \n108177 176.88 125.32 9942.25095 \n108343 68.51 34.49 1457.96785 \n109094 44.07 66.41 2069.93800 \n2190513 105.27 139.55 7696.12525 \n\n[20607 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "1d053d8ce8b24c67aa71cdd82d42465e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1d6d3b6ca031443a9d4de538dee1f120": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_61f223b762454f7abf68e92c3f05f8ec", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
00person1valid384.43172.2115.1235.74435.14495
10person1valid412.80157.6153.05138.012913.11040
20chair62valid290.69218.0061.8398.481833.78400
30chair62valid317.40219.2421.5811.59210.14820
40chair62valid358.98218.0556.00102.832245.34355
..............................
367764999banana52valid439.3394.35160.05171.8616690.94945
367774999banana52valid467.23280.45172.77177.6322016.99120
367784999banana52valid467.750.0070.4125.881191.12265
367794999banana52valid561.816.8778.1134.131736.78505
367804999banana52valid582.44141.9157.5686.751752.08170
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 0 person 1 valid 384.43 172.21 \n1 0 person 1 valid 412.80 157.61 \n2 0 chair 62 valid 290.69 218.00 \n3 0 chair 62 valid 317.40 219.24 \n4 0 chair 62 valid 358.98 218.05 \n... ... ... ... ... ... ... \n36776 4999 banana 52 valid 439.33 94.35 \n36777 4999 banana 52 valid 467.23 280.45 \n36778 4999 banana 52 valid 467.75 0.00 \n36779 4999 banana 52 valid 561.81 6.87 \n36780 4999 banana 52 valid 582.44 141.91 \n\n box_width box_height area \nid \n0 15.12 35.74 435.14495 \n1 53.05 138.01 2913.11040 \n2 61.83 98.48 1833.78400 \n3 21.58 11.59 210.14820 \n4 56.00 102.83 2245.34355 \n... ... ... ... \n36776 160.05 171.86 16690.94945 \n36777 172.77 177.63 22016.99120 \n36778 70.41 25.88 1191.12265 \n36779 78.11 34.13 1736.78505 \n36780 57.56 86.75 1752.08170 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "1e377c71b8164868a36e6a22cd9f1980": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_bd34f01e287a47d79a85c4dfb923c687", "style": "IPY_MODEL_9ab1868fedf14174836eee31d5aefa66", "value": "

Dataset object containing 2 images and 0 object\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "1e78149cc3e44af495d17c80b0e0015b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_945a36e7a2b34d4f933d295211f919ec", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

4722 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[4722 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "1ea85e14ffbd4a48aa549195fc5171ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1ffadd66d4824428bf4bd02321a8baf0": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_6bed46054c494a0cafee9c2d8966c99e", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589740113354zebra24valid366.49174.59115.67142.715784.68620
46790558393person1valid342.52163.33192.2477.475408.64720
171099058393person1valid418.99182.6561.1245.001792.80770
..............................
1101888428280laptop73valid118.71138.3444.7935.831405.98065
2190513428280chair62valid203.58179.65105.27139.557696.12525
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

18391 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589740 113354 zebra 24 valid 366.49 174.59 \n467905 58393 person 1 valid 342.52 163.33 \n1710990 58393 person 1 valid 418.99 182.65 \n... ... ... ... ... ... ... \n1101888 428280 laptop 73 valid 118.71 138.34 \n2190513 428280 chair 62 valid 203.58 179.65 \n331107 349837 refrigerator 82 valid 66.00 94.87 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n602093 85.89 40.67 2605.72090 \n589740 115.67 142.71 5784.68620 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n... ... ... ... \n1101888 44.79 35.83 1405.98065 \n2190513 105.27 139.55 7696.12525 \n331107 71.25 194.26 12029.44125 \n333394 113.49 298.65 30406.51295 \n333731 39.61 328.64 12492.52165 \n\n[18391 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "201c0ac6623f46f2a1940f2ff1fbfa65": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_a0530d16806447fb887a285e3298f421", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "20e1a2357cb34893b0a962c96485a5e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "211946f8bec547cc8dc5f8c2b8e9af77": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "215230f60dc54b66975684b3962c0042": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_5de7aa7b0bf149cab034f198eeac4ba7", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
535917352582person1valid0.00256.0080.54376.8122650.73800
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
..............................
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
332788349837refrigerator82valid138.0062.6398.25252.0023037.46875
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333685349837refrigerator82valid335.0410.48125.17318.7837187.97840
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589077 113354 zebra 24 valid 260.99 158.88 \n589740 113354 zebra 24 valid 366.49 174.59 \n... ... ... ... ... ... ... \n331107 349837 refrigerator 82 valid 66.00 94.87 \n332788 349837 refrigerator 82 valid 138.00 62.63 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333685 349837 refrigerator 82 valid 335.04 10.48 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n589077 141.52 194.11 9978.94125 \n589740 115.67 142.71 5784.68620 \n... ... ... ... \n331107 71.25 194.26 12029.44125 \n332788 98.25 252.00 23037.46875 \n333394 113.49 298.65 30406.51295 \n333685 125.17 318.78 37187.97840 \n333731 39.61 328.64 12492.52165 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "2315e499822d468c8925f1d42b700cf9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "24c3935dccad491d92f92a9a01fa5f37": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "25e0d0eb9c2c40488365ce8a27295bfc": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_164a53399de6479db15055e945ecbdd1", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person15valid112.43195.32214.78438.1948685.67910
535917352582person15valid0.00256.0080.54376.8122650.73800
46790558393person15valid342.52163.33192.2477.475408.64720
171099058393person15valid418.99182.6561.1245.001792.80770
1238519147729person15valid0.0087.01310.67287.9955847.52705
..............................
1192747105455car7valid333.56517.2117.4314.58182.21680
108177428280chair9valid3.58189.14176.88125.329942.25095
108343428280chair9valid244.05152.2968.5134.491457.96785
109094428280chair9valid187.71114.8044.0766.412069.93800
2190513428280chair9valid203.58179.65105.27139.557696.12525
\n

20950 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 15 valid 112.43 195.32 \n535917 352582 person 15 valid 0.00 256.00 \n467905 58393 person 15 valid 342.52 163.33 \n1710990 58393 person 15 valid 418.99 182.65 \n1238519 147729 person 15 valid 0.00 87.01 \n... ... ... ... ... ... ... \n1192747 105455 car 7 valid 333.56 517.21 \n108177 428280 chair 9 valid 3.58 189.14 \n108343 428280 chair 9 valid 244.05 152.29 \n109094 428280 chair 9 valid 187.71 114.80 \n2190513 428280 chair 9 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n1238519 310.67 287.99 55847.52705 \n... ... ... ... \n1192747 17.43 14.58 182.21680 \n108177 176.88 125.32 9942.25095 \n108343 68.51 34.49 1457.96785 \n109094 44.07 66.41 2069.93800 \n2190513 105.27 139.55 7696.12525 \n\n[20950 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "25f0fc1cdb4d4ced8da828dad1538444": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2688ffd92c114b6db55756eb9490056b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "270e2dd454114fa191b7a78f5cd0f442": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2765d25940f94154ab972f0a4163552f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_c7a7060a8aa7446299af9cc8d1cab19e", "IPY_MODEL_6e2136aa5aa042c580f2cd537e09932c" ], "layout": "IPY_MODEL_6682ee61f7a54c9980d7fe0d8edf23c9" } }, "276b9e6a229d40f1bf0e2dfbe6aa5394": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_d5e3a1dbf17e4f24bbac1e79e3ccea3a", "IPY_MODEL_7651b2578bad44ea89843819afce536b" ], "layout": "IPY_MODEL_4d6380718f7241499844d16e3f92fe3a" } }, "283b8c8563c44ee186d9bbdc7fabceae": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_b7a7836c0928481eb773f233fab0a635", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
229airplane5valid282.23139.3851.2243.78567.94110
329airplane5valid150.178.6374.0977.191454.47215
461airplane5valid9.43105.72621.76183.1540544.34585
561airplane5valid51.40249.06250.4428.092415.22505
6139airplane5valid0.00142.01566.13143.9430298.91215
..............................
367784838zebra24valid70.112.16433.62434.69122687.61775
367794966zebra24valid107.31141.6479.67159.545946.64725
367804966zebra24valid342.29164.82224.42222.7823360.62700
367814966zebra24valid418.15164.9182.4663.513431.98040
367824966zebra24valid190.56143.9161.39172.455648.15435
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n2 29 airplane 5 valid 282.23 139.38 \n3 29 airplane 5 valid 150.17 8.63 \n4 61 airplane 5 valid 9.43 105.72 \n5 61 airplane 5 valid 51.40 249.06 \n6 139 airplane 5 valid 0.00 142.01 \n... ... ... ... ... ... ... \n36778 4838 zebra 24 valid 70.11 2.16 \n36779 4966 zebra 24 valid 107.31 141.64 \n36780 4966 zebra 24 valid 342.29 164.82 \n36781 4966 zebra 24 valid 418.15 164.91 \n36782 4966 zebra 24 valid 190.56 143.91 \n\n box_width box_height area \nid \n2 51.22 43.78 567.94110 \n3 74.09 77.19 1454.47215 \n4 621.76 183.15 40544.34585 \n5 250.44 28.09 2415.22505 \n6 566.13 143.94 30298.91215 \n... ... ... ... \n36778 433.62 434.69 122687.61775 \n36779 79.67 159.54 5946.64725 \n36780 224.42 222.78 23360.62700 \n36781 82.46 63.51 3431.98040 \n36782 61.39 172.45 5648.15435 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "2960d6dcc47d49a381e77ae2aa56613d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_4fc4c1800ef645c293daa0d77c993f92", "IPY_MODEL_ccbe6d78c4684bbe927f1efdbdc0f52d", "IPY_MODEL_61f1d9cd68e047468f7e6476f0e8a038" ], "layout": "IPY_MODEL_c497c29f8fa5485fb878086fd794cf28", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "29c931608dbf4121b1cbb276ae817e39": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2a3ca1e55ef24e72b351e4d45e0d7d53": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_ea17d1aba9cb4d4cac2d795b5295c04c", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
0640426Images/valid/000000000139.jpg.jpgvalid
1586640Images/valid/000000000285.jpg.jpgvalid
2640483Images/valid/000000000632.jpg.jpgvalid
3375500Images/valid/000000000724.jpg.jpgvalid
4428640Images/valid/000000000776.jpg.jpgvalid
..................
4995640354Images/valid/000000581317.jpg.jpgvalid
4996612612Images/valid/000000581357.jpg.jpgvalid
4997640427Images/valid/000000581482.jpg.jpgvalid
4998478640Images/valid/000000581615.jpg.jpgvalid
4999640478Images/valid/000000581781.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n0 640 426 Images/valid/000000000139.jpg .jpg valid\n1 586 640 Images/valid/000000000285.jpg .jpg valid\n2 640 483 Images/valid/000000000632.jpg .jpg valid\n3 375 500 Images/valid/000000000724.jpg .jpg valid\n4 428 640 Images/valid/000000000776.jpg .jpg valid\n... ... ... ... ... ...\n4995 640 354 Images/valid/000000581317.jpg .jpg valid\n4996 612 612 Images/valid/000000581357.jpg .jpg valid\n4997 640 427 Images/valid/000000581482.jpg .jpg valid\n4998 478 640 Images/valid/000000581615.jpg .jpg valid\n4999 640 478 Images/valid/000000581781.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "2a9067f1f71a45dea576b74070e26373": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2cf5d9a88c3247949c850946da75ea68": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2d24a8f5bd594fc3a8d31f3474631e19": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_41d1fb5f8ec24526abd47402a099866c", "IPY_MODEL_b25e07525da2464e96bc0df015a82213", "IPY_MODEL_ac087e9ac3c140c79ea14a6e6aaffbb7" ], "layout": "IPY_MODEL_437c53d37a254225b1c2780895aa6178", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "2d7db77130b54cafb143a1b7c507fa71": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7bf208ab93ca491faa2b1a31d26647b8", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "2eca2d773c854eefba61ef50e9161d37": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2ecdad4b4e9541b3a2ba68727f5906b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "2eda4e5db5ac43819b8bbcb8291987d2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_85f426e2ee5c45d5bec8cd768186b99c", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "2fce65d13d27434db3c945733110feba": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_91e96815232c44b5835ed421e1516dca", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
113354640480Images/valid/000000113354.jpg.jpgvalid
\n
", "text/plain": " width height relative_path type split\nid \n113354 640 480 Images/valid/000000113354.jpg .jpg valid" }, "metadata": {}, "output_type": "display_data" } ] } }, "301211805d4c40f580e4d61f9e359404": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "31c5d5f134534aa2b95fe24095c20404": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "31e84d0a79824ce9a92d05234d3306bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3209edc462354a22a7d772253934a13c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "33dd6b044ff448fb8de7b1e2d0f8cf58": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "33e8c53a668c4f61bfde88a0a536ea24": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_5fec21e49c344cbc9c63d4c1d51cd4bb", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
519208640406Images/valid/000000519208.jpg.jpgvalid
38048299500Images/valid/000000038048.jpg.jpgvalid
..................
286708640481Images/valid/000000286708.jpg.jpgvalid
507893427640Images/valid/000000507893.jpg.jpgvalid
524280640640Images/valid/000000524280.jpg.jpgvalid
344059640427Images/valid/000000344059.jpg.jpgvalid
311295640427Images/valid/000000311295.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n519208 640 406 Images/valid/000000519208.jpg .jpg valid\n38048 299 500 Images/valid/000000038048.jpg .jpg valid\n... ... ... ... ... ...\n286708 640 481 Images/valid/000000286708.jpg .jpg valid\n507893 427 640 Images/valid/000000507893.jpg .jpg valid\n524280 640 640 Images/valid/000000524280.jpg .jpg valid\n344059 640 427 Images/valid/000000344059.jpg .jpg valid\n311295 640 427 Images/valid/000000311295.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "3542af2d452c45d9a2de907af0446c5c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_98e9c540954743faacdc7b8ddea8124a", "IPY_MODEL_c6f7a9fda097487d8e8c50b0cacdf846" ], "layout": "IPY_MODEL_e10607eb5ef04569a4c9a797967b2da8" } }, "357957009e864da7ae317388a2ca3d36": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_f5df0a434c4f4e739d799df0d6930bf8", "style": "IPY_MODEL_3e34ae6be7b2489d9142f8e1b0946796", "value": "

Dataset object containing 5,000 images and 20,950 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "35c08fe2ab8f4a2ab98fc4dbc7902325": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_ecf576b29a014db481e9829081b09c68", "IPY_MODEL_283b8c8563c44ee186d9bbdc7fabceae", "IPY_MODEL_ec10a02b68d543e2a7202f4f193def5c" ], "layout": "IPY_MODEL_b75cc350015f4ed68787675876e5fc41", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "36ea5bee58d347cb943a1e3773c9b1a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "3705bba3da4b4d8888ddf8632a4ef809": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_aaf31450ec344590a7e1bc8e6c8260ce", "IPY_MODEL_bf859b3043c14f999f630d7d9657d4b1" ], "layout": "IPY_MODEL_f8bb9c3a15c947268a2a91f733da3611" } }, "386e20fbf47c415bb2b40866aca1044d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_81cbd34f476d414db06011869f6c9433", "style": "IPY_MODEL_893459f11983415abc20160387905a2a", "value": "

Dataset object containing 4,722 images and 34,818 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "3998d3b3576e4118b902136127fa1523": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_1ab75e49bbbc4821a82133fbe61f062e", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "3ab115aa46224d2a85191285da2cb442": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3b12a2ff1760404eb9d0f402e98dcd25": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3e34ae6be7b2489d9142f8e1b0946796": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "3ea45c5b477b49308bb0e428284e33f5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3f8d9a7fce6b45e5b6a4816b261a57aa": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_c17e5469e14e45389ae5670a74d3fd6b", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
519208640406Images/valid/000000519208.jpg.jpgvalid
38048299500Images/valid/000000038048.jpg.jpgvalid
..................
185409640424Images/valid/000000185409.jpg.jpgvalid
577976640428Images/valid/000000577976.jpg.jpgvalid
363188640425Images/valid/000000363188.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
\n

2500 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n519208 640 406 Images/valid/000000519208.jpg .jpg valid\n38048 299 500 Images/valid/000000038048.jpg .jpg valid\n... ... ... ... ... ...\n185409 640 424 Images/valid/000000185409.jpg .jpg valid\n577976 640 428 Images/valid/000000577976.jpg .jpg valid\n363188 640 425 Images/valid/000000363188.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n\n[2500 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "41d1fb5f8ec24526abd47402a099866c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_5e66223c872e4da2ac883f200de13e76", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid" }, "metadata": {}, "output_type": "display_data" } ] } }, "42ec456df0704ce7b9e76650e63624c6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f1b510f4d8bb4bba8ad06e2e2a53619f", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
00chair62valid244.82230.45104.7287.695184.22260
10potted plant64valid183.36136.5660.7892.392464.93305
20potted plant64valid347.35212.3782.51143.007034.62185
30bed65valid3.27266.85401.23208.2564019.87940
40book84valid416.0043.00153.00303.0020933.00000
..............................
367764997skateboard41valid128.77152.7250.0017.21364.30040
367774998person1valid0.002.53469.53215.2653359.63985
367784998hot dog58valid39.91140.73308.31232.6046341.95470
367794998hot dog58valid147.27282.15246.36206.4514878.08055
367804999toilet70valid139.32386.03191.56235.1034120.51835
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 0 chair 62 valid 244.82 230.45 \n1 0 potted plant 64 valid 183.36 136.56 \n2 0 potted plant 64 valid 347.35 212.37 \n3 0 bed 65 valid 3.27 266.85 \n4 0 book 84 valid 416.00 43.00 \n... ... ... ... ... ... ... \n36776 4997 skateboard 41 valid 128.77 152.72 \n36777 4998 person 1 valid 0.00 2.53 \n36778 4998 hot dog 58 valid 39.91 140.73 \n36779 4998 hot dog 58 valid 147.27 282.15 \n36780 4999 toilet 70 valid 139.32 386.03 \n\n box_width box_height area \nid \n0 104.72 87.69 5184.22260 \n1 60.78 92.39 2464.93305 \n2 82.51 143.00 7034.62185 \n3 401.23 208.25 64019.87940 \n4 153.00 303.00 20933.00000 \n... ... ... ... \n36776 50.00 17.21 364.30040 \n36777 469.53 215.26 53359.63985 \n36778 308.31 232.60 46341.95470 \n36779 246.36 206.45 14878.08055 \n36780 191.56 235.10 34120.51835 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "42f49954f3374d9db6e010ab6ad3e6cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4348de50280a44f8a13045980f827e59": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_ccd0c39ae0d646ff9987e51710d01995", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1aeroplane
2bicycle
3bird
4boat
5bottle
6bus
7car
8cat
9chair
10cow
11diningtable
12dog
13horse
14motorbike
15person
16pottedplant
17sheep
18sofa
19train
20tvmonitor
21something else
\n
", "text/plain": " category string\ncategory_id \n1 aeroplane\n2 bicycle\n3 bird\n4 boat\n5 bottle\n6 bus\n7 car\n8 cat\n9 chair\n10 cow\n11 diningtable\n12 dog\n13 horse\n14 motorbike\n15 person\n16 pottedplant\n17 sheep\n18 sofa\n19 train\n20 tvmonitor\n21 something else" }, "metadata": {}, "output_type": "display_data" } ] } }, "437c53d37a254225b1c2780895aa6178": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "444834df6a7744f28333fc00e08f015e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_5637e06d76514666b1d7fcf8d7cca48f", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "4451e043cc5a4137b2ee99911d110707": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_1e78149cc3e44af495d17c80b0e0015b", "IPY_MODEL_026b7cd5221b4ea3baf71572d7437064", "IPY_MODEL_a799764dd9924208b78132c873ae60c0" ], "layout": "IPY_MODEL_98dc851e52e94d1682bf1c559530db18", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "44adcddaa7994223b957a8385b296bc9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_0e3375a643124cf9bbe53c4c807fde5f", "style": "IPY_MODEL_d6fe810a673b4df3b2dee940a9ca2897", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "46848b28dac8460abe51743163af9939": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_0c28b3bd2999413286338ace0fc5d4f7", "IPY_MODEL_d2a60949036846628bae224850cb21e5" ], "layout": "IPY_MODEL_0a41d889cee04896b371be88fb621959" } }, "46e2e51db1ef4897ba11de653a89fd50": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "47f377ac62584f86894264ba0e7c570d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "48d1ff71f56c4763a7ac4d3736fbddf1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_f288611e5cda425387ea6350e30e1af2", "IPY_MODEL_ccc69999bf004cdcbde3354d74c78850", "IPY_MODEL_3998d3b3576e4118b902136127fa1523" ], "layout": "IPY_MODEL_b60cb52cd3e4493283ae46259ea964f1", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "494d15aa83c24127ac87b30c627df20b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4af4df92ffd74f19a264c6298fec7326": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4b5120a0e8274f3babc93743e1e22543": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_5a2c3538561d4536a1a05f56c6f27729", "style": "IPY_MODEL_f29e02fa480a4e51b63c85f5a07560dc", "value": "

Dataset object containing 5,000 images and 41,900 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "4c156340353a448aa26c07d00f3534ee": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_db244fd778374cd9a420b62f45288859", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person15valid112.43195.32214.78438.1948685.67910
535917352582person15valid0.00256.0080.54376.8122650.73800
46790558393person15valid342.52163.33192.2477.475408.64720
171099058393person15valid418.99182.6561.1245.001792.80770
1238519147729person15valid0.0087.01310.67287.9955847.52705
..............................
906701608203105455car7valid333.56517.2117.4314.58182.21680
906700523633428280chair9valid3.58189.14176.88125.329942.25095
906700523799428280chair9valid244.05152.2968.5134.491457.96785
906700524550428280chair9valid187.71114.8044.0766.412069.93800
906702605969428280chair9valid203.58179.65105.27139.557696.12525
\n

41900 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 15 valid 112.43 195.32 \n535917 352582 person 15 valid 0.00 256.00 \n467905 58393 person 15 valid 342.52 163.33 \n1710990 58393 person 15 valid 418.99 182.65 \n1238519 147729 person 15 valid 0.00 87.01 \n... ... ... ... ... ... ... \n906701608203 105455 car 7 valid 333.56 517.21 \n906700523633 428280 chair 9 valid 3.58 189.14 \n906700523799 428280 chair 9 valid 244.05 152.29 \n906700524550 428280 chair 9 valid 187.71 114.80 \n906702605969 428280 chair 9 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n1238519 310.67 287.99 55847.52705 \n... ... ... ... \n906701608203 17.43 14.58 182.21680 \n906700523633 176.88 125.32 9942.25095 \n906700523799 68.51 34.49 1457.96785 \n906700524550 44.07 66.41 2069.93800 \n906702605969 105.27 139.55 7696.12525 \n\n[41900 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "4c5b28016ec346c8978de6c783d973ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4d6380718f7241499844d16e3f92fe3a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4e9e02be8d9f47a883d4b2e4c7dd3f5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4f24b4c99bbd4bb986f611bdc3cb0cc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_9f7b0de8a9b044f0a71fb2f1470c8821", "IPY_MODEL_a76843a616534c5a84fda9a53ae68fc7", "IPY_MODEL_154a1b375375452988f208e85549c375" ], "layout": "IPY_MODEL_6260ada7134c4ecf9439b463e1f5f7ce", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "4f56193640fa4ba2aad64ba1d62e31f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4f94b08d95494bd1937c5e54d3b73942": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_211946f8bec547cc8dc5f8c2b8e9af77", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "4fa27b89ba544bd1ac84c2e3179e827f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "4fc4c1800ef645c293daa0d77c993f92": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_e345619f1ff841aeb526c74d4a9afa99", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "4fe4608ef6e34b44a3d0cd32ccd2b5b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "519250c97654434ca2363551c820cc58": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "51b02e3991f24fd6b09d0dbe65efc24f": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_9c627853f4364595b494460cc3f26a45", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
357584310072Vehicle2valid223.850.00258.8369.0814718.47935
358258310072Vehicle2valid0.001.94135.9966.276989.84750
363154310072Vehicle2valid170.890.0071.0636.961729.26630
1337431310072Vehicle2valid631.997.358.0115.4691.97330
135610638048Vehicle2valid78.67202.2412.398.7280.88790
..............................
1192747105455Vehicle2valid333.56517.2117.4314.58182.21680
108177428280Object3valid3.58189.14176.88125.329942.25095
108343428280Object3valid244.05152.2968.5134.491457.96785
109094428280Object3valid187.71114.8044.0766.412069.93800
2190513428280Object3valid203.58179.65105.27139.557696.12525
\n

9946 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n357584 310072 Vehicle 2 valid 223.85 0.00 \n358258 310072 Vehicle 2 valid 0.00 1.94 \n363154 310072 Vehicle 2 valid 170.89 0.00 \n1337431 310072 Vehicle 2 valid 631.99 7.35 \n1356106 38048 Vehicle 2 valid 78.67 202.24 \n... ... ... ... ... ... ... \n1192747 105455 Vehicle 2 valid 333.56 517.21 \n108177 428280 Object 3 valid 3.58 189.14 \n108343 428280 Object 3 valid 244.05 152.29 \n109094 428280 Object 3 valid 187.71 114.80 \n2190513 428280 Object 3 valid 203.58 179.65 \n\n box_width box_height area \nid \n357584 258.83 69.08 14718.47935 \n358258 135.99 66.27 6989.84750 \n363154 71.06 36.96 1729.26630 \n1337431 8.01 15.46 91.97330 \n1356106 12.39 8.72 80.88790 \n... ... ... ... \n1192747 17.43 14.58 182.21680 \n108177 176.88 125.32 9942.25095 \n108343 68.51 34.49 1457.96785 \n109094 44.07 66.41 2069.93800 \n2190513 105.27 139.55 7696.12525 \n\n[9946 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "5224138b18e0495f9b681b7636e508a6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f7fb857318714b95b6a4cadc2be3f42c", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
3053425640Images/valid/000000352582.jpg.jpgvalid
967640480Images/valid/000000113354.jpg.jpgvalid
524640486Images/valid/000000058393.jpg.jpgvalid
1260500375Images/valid/000000147729.jpg.jpgvalid
2695640383Images/valid/000000310072.jpg.jpgvalid
..................
2702480640Images/valid/000000311180.jpg.jpgvalid
2626640359Images/valid/000000302030.jpg.jpgvalid
905427640Images/valid/000000105455.jpg.jpgvalid
3705500333Images/valid/000000428280.jpg.jpgvalid
3028500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n3053 425 640 Images/valid/000000352582.jpg .jpg valid\n967 640 480 Images/valid/000000113354.jpg .jpg valid\n524 640 486 Images/valid/000000058393.jpg .jpg valid\n1260 500 375 Images/valid/000000147729.jpg .jpg valid\n2695 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n2702 480 640 Images/valid/000000311180.jpg .jpg valid\n2626 640 359 Images/valid/000000302030.jpg .jpg valid\n905 427 640 Images/valid/000000105455.jpg .jpg valid\n3705 500 333 Images/valid/000000428280.jpg .jpg valid\n3028 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "53b65b08631e4456a44b729be6620421": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "550e373b38a54c63974397f70115ae35": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_ecc7c3f906844b789a62761ee82aeaa9", "IPY_MODEL_5e25cb3d316940da91e387f91394de24", "IPY_MODEL_96b0b9ebdf5d49278fcc636d5fc1bdb3" ], "layout": "IPY_MODEL_e2d3753a75aa4fb1924f269175afeeab", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "552731e964f94279afc56c9dcd2b189c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_16504d84d20a4054ac3cecaf37bde11d", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "56261b59834f402d86eb8a3bcdae35d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_9892b77e263a4a86a7d43102ddf3268f", "style": "IPY_MODEL_13a7f428c4b34e439869a71eef1664dd", "value": "

Dataset object containing 5,000 images and 20,607 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "5637e06d76514666b1d7fcf8d7cca48f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "56b97a3e4d4745c9941c7551b6d5922d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_6e3740ae905e42c08c6c1bca288da9ab", "IPY_MODEL_8c824c73911a4b55afcd739b89c329be" ], "layout": "IPY_MODEL_ea276594fe214985ae130d548ca3874a" } }, "596a11fbefff42649404d420ef29f5b4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "5a2c3538561d4536a1a05f56c6f27729": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5bd0b17b9d4e4008939ba492c6a5377a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5bfb24a8b50e45cf952978da02f50a3e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_6e7c50674eb54e0eb6542ae4a1c674e9", "style": "IPY_MODEL_0de44c2755cc44d698d99fb3254ea773", "value": "

Dataset object containing 5,000 images and 20,607 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "5bfc0fd678c2438e992a5d4eca371e60": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "5c42c424c9dd4c08bd87e0f7e307e3fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_44adcddaa7994223b957a8385b296bc9", "IPY_MODEL_cda0463595624e2b80fb4a544b051fe8" ], "layout": "IPY_MODEL_deed7abbfb4942c1bc39b66fb3b7efe4" } }, "5de7aa7b0bf149cab034f198eeac4ba7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5e25cb3d316940da91e387f91394de24": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_fcc69010130c4e48b06ad36220cd6afe", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
535917352582person1valid0.00256.0080.54376.8122650.73800
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
..............................
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
332788349837refrigerator82valid138.0062.6398.25252.0023037.46875
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333685349837refrigerator82valid335.0410.48125.17318.7837187.97840
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

34818 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589077 113354 zebra 24 valid 260.99 158.88 \n589740 113354 zebra 24 valid 366.49 174.59 \n... ... ... ... ... ... ... \n331107 349837 refrigerator 82 valid 66.00 94.87 \n332788 349837 refrigerator 82 valid 138.00 62.63 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333685 349837 refrigerator 82 valid 335.04 10.48 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n589077 141.52 194.11 9978.94125 \n589740 115.67 142.71 5784.68620 \n... ... ... ... \n331107 71.25 194.26 12029.44125 \n332788 98.25 252.00 23037.46875 \n333394 113.49 298.65 30406.51295 \n333685 125.17 318.78 37187.97840 \n333731 39.61 328.64 12492.52165 \n\n[34818 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "5e66223c872e4da2ac883f200de13e76": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5fb86192340c4526a85900dff25de564": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "5fec21e49c344cbc9c63d4c1d51cd4bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "612ca0d15721443686599d046a2aae92": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "61e34fb2b60a401785348f10aff29d49": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_3f8d9a7fce6b45e5b6a4816b261a57aa", "IPY_MODEL_9beaa6e96d8c404f80fdd3c9ddcfb569", "IPY_MODEL_201c0ac6623f46f2a1940f2ff1fbfa65" ], "layout": "IPY_MODEL_c6a12a1635ad495cb5d87a4f8c586bfb", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "61f1d9cd68e047468f7e6476f0e8a038": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7e414c835aa845c8959bb9cad0ac41f7", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1something else
2bicycle
3bird
4boat
5bottle
6bus
7car
8cat
9chair
10cow
11diningtable
12dog
13horse
14motorbike
15person
16pottedplant
17sheep
18sofa
19train
20tvmonitor
21aeroplane
\n
", "text/plain": " category string\ncategory_id \n1 something else\n2 bicycle\n3 bird\n4 boat\n5 bottle\n6 bus\n7 car\n8 cat\n9 chair\n10 cow\n11 diningtable\n12 dog\n13 horse\n14 motorbike\n15 person\n16 pottedplant\n17 sheep\n18 sofa\n19 train\n20 tvmonitor\n21 aeroplane" }, "metadata": {}, "output_type": "display_data" } ] } }, "61f223b762454f7abf68e92c3f05f8ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6260ada7134c4ecf9439b463e1f5f7ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "62a01d8f832140b98d3ff6af30de8e5b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "63b646f5fe4b44e5a9eb540679b38179": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "63d68d45e3cc4478ac890817f2e48433": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_2ecdad4b4e9541b3a2ba68727f5906b1", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1aeroplane
2bicycle
3bird
4boat
5bottle
6bus
7car
8cat
9chair
10cow
11diningtable
12dog
13horse
14motorbike
15person
16pottedplant
17sheep
18sofa
19train
20tvmonitor
2121
\n
", "text/plain": " category string\ncategory_id \n1 aeroplane\n2 bicycle\n3 bird\n4 boat\n5 bottle\n6 bus\n7 car\n8 cat\n9 chair\n10 cow\n11 diningtable\n12 dog\n13 horse\n14 motorbike\n15 person\n16 pottedplant\n17 sheep\n18 sofa\n19 train\n20 tvmonitor\n21 21" }, "metadata": {}, "output_type": "display_data" } ] } }, "64846a560b0a4f4ebdd1e0f811fa78f0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_f32c7f63262042e18b56852b902d40c0", "style": "IPY_MODEL_4fa27b89ba544bd1ac84c2e3179e827f", "value": "

Dataset object containing 2,500 images and 18,670 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "648ed8ee168c4e6a839ca4bbebcab214": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "65527049e5a7455e9598477f43a0cfd2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_5224138b18e0495f9b681b7636e508a6", "IPY_MODEL_67c85ec5354c4e9e8ffd53ea9acc0b11", "IPY_MODEL_79f9cc5c06fb4eba9ba0de8566cdf87a" ], "layout": "IPY_MODEL_729e431035174cacae488a83a622db8a", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "6682ee61f7a54c9980d7fe0d8edf23c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "67c85ec5354c4e9e8ffd53ea9acc0b11": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_270e2dd454114fa191b7a78f5cd0f442", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
4604503053person1valid112.43195.32214.78438.1948685.67910
5359173053person1valid0.00256.0080.54376.8122650.73800
6020933053frisbee34valid171.63424.0385.8940.672605.72090
589077967zebra24valid260.99158.88141.52194.119978.94125
589740967zebra24valid366.49174.59115.67142.715784.68620
..............................
3311073028refrigerator82valid66.0094.8771.25194.2612029.44125
3327883028refrigerator82valid138.0062.6398.25252.0023037.46875
3333943028refrigerator82valid234.4429.87113.49298.6530406.51295
3336853028refrigerator82valid335.0410.48125.17318.7837187.97840
3337313028refrigerator82valid460.390.0039.61328.6412492.52165
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 3053 person 1 valid 112.43 195.32 \n535917 3053 person 1 valid 0.00 256.00 \n602093 3053 frisbee 34 valid 171.63 424.03 \n589077 967 zebra 24 valid 260.99 158.88 \n589740 967 zebra 24 valid 366.49 174.59 \n... ... ... ... ... ... ... \n331107 3028 refrigerator 82 valid 66.00 94.87 \n332788 3028 refrigerator 82 valid 138.00 62.63 \n333394 3028 refrigerator 82 valid 234.44 29.87 \n333685 3028 refrigerator 82 valid 335.04 10.48 \n333731 3028 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n589077 141.52 194.11 9978.94125 \n589740 115.67 142.71 5784.68620 \n... ... ... ... \n331107 71.25 194.26 12029.44125 \n332788 98.25 252.00 23037.46875 \n333394 113.49 298.65 30406.51295 \n333685 125.17 318.78 37187.97840 \n333731 39.61 328.64 12492.52165 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "68d2c10291ca463ab2cf05746ca09093": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "6b694a522a394ca9b67905fac307a804": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_0ed3d4a3ff4949cb924eb8df126f35ec", "IPY_MODEL_c0624f6057fb4ff99ed6509a042305ab" ], "layout": "IPY_MODEL_dd35663bc1bb459985d75e20a0f2885d" } }, "6b87f1dc969a461997795cc9c83e9312": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6bed46054c494a0cafee9c2d8966c99e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6d671c481a0946cd93a7b18099a60178": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_adb47fe414d54c978a5f98dd7a48f48e", "IPY_MODEL_c1569471bd79471f9f571bd1e87ccf78", "IPY_MODEL_770a97b7e9c346799bd1275a523fc26d" ], "layout": "IPY_MODEL_c7aea4c13f6f4566a773c0c3cc2be09c", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "6e2136aa5aa042c580f2cd537e09932c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_2fce65d13d27434db3c945733110feba", "IPY_MODEL_b2c77bc7409b41af9dbe153b7d7df4da", "IPY_MODEL_4f94b08d95494bd1937c5e54d3b73942" ], "layout": "IPY_MODEL_519250c97654434ca2363551c820cc58", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "6e3740ae905e42c08c6c1bca288da9ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_e7ea818c8348466b9b548a435e1af377", "style": "IPY_MODEL_596a11fbefff42649404d420ef29f5b4", "value": "

Dataset object containing 5,000 images and 18,390 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "6e7c50674eb54e0eb6542ae4a1c674e9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6ec0501159b94ca7aaeac2588c2e0a9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6fef19ee787a402db9fbcc046ee65a2f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "706174f71ed04da59539226646743f27": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_444834df6a7744f28333fc00e08f015e", "IPY_MODEL_1cf069555e2548a9919cb3015944892c", "IPY_MODEL_f628d827f3494e61a5e13c72567f8983" ], "layout": "IPY_MODEL_03612feab58e4d9aabc7ddeebd96c2d3", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "70a610378d7c4d968d72b37013edc46b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "729e431035174cacae488a83a622db8a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "739b133f251f4d3bacfeda8ad058349a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "73a2a103d75245dfb95b7c238620db6e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_2eca2d773c854eefba61ef50e9161d37", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589740113354zebra24valid366.49174.59115.67142.715784.68620
46790558393person1valid342.52163.33192.2477.475408.64720
171099058393person1valid418.99182.6561.1245.001792.80770
..............................
1101888428280laptop73valid118.71138.3444.7935.831405.98065
2190513428280chair62valid203.58179.65105.27139.557696.12525
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

18391 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589740 113354 zebra 24 valid 366.49 174.59 \n467905 58393 person 1 valid 342.52 163.33 \n1710990 58393 person 1 valid 418.99 182.65 \n... ... ... ... ... ... ... \n1101888 428280 laptop 73 valid 118.71 138.34 \n2190513 428280 chair 62 valid 203.58 179.65 \n331107 349837 refrigerator 82 valid 66.00 94.87 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n602093 85.89 40.67 2605.72090 \n589740 115.67 142.71 5784.68620 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n... ... ... ... \n1101888 44.79 35.83 1405.98065 \n2190513 105.27 139.55 7696.12525 \n331107 71.25 194.26 12029.44125 \n333394 113.49 298.65 30406.51295 \n333731 39.61 328.64 12492.52165 \n\n[18391 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "7624ce323ee04e1c9158288ba8c702ef": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_46e2e51db1ef4897ba11de653a89fd50", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
535917352582person1valid0.00256.0080.54376.8122650.73800
602093352582frisbee34valid171.63424.0385.8940.672605.72090
46790558393person1valid342.52163.33192.2477.475408.64720
57690058393bench15valid44.78242.27547.16224.9882291.54995
..............................
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
332788349837refrigerator82valid138.0062.6398.25252.0023037.46875
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333685349837refrigerator82valid335.0410.48125.17318.7837187.97840
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n467905 58393 person 1 valid 342.52 163.33 \n576900 58393 bench 15 valid 44.78 242.27 \n... ... ... ... ... ... ... \n331107 349837 refrigerator 82 valid 66.00 94.87 \n332788 349837 refrigerator 82 valid 138.00 62.63 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333685 349837 refrigerator 82 valid 335.04 10.48 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n467905 192.24 77.47 5408.64720 \n576900 547.16 224.98 82291.54995 \n... ... ... ... \n331107 71.25 194.26 12029.44125 \n332788 98.25 252.00 23037.46875 \n333394 113.49 298.65 30406.51295 \n333685 125.17 318.78 37187.97840 \n333731 39.61 328.64 12492.52165 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "7651b2578bad44ea89843819afce536b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_fa468d8ea1954346ba62e1f3ba16da9d", "IPY_MODEL_42ec456df0704ce7b9e76650e63624c6", "IPY_MODEL_01f868101b094c23b48e203b66298705" ], "layout": "IPY_MODEL_c74870f966e84cdf8b0134024009119e", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "770a97b7e9c346799bd1275a523fc26d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_1a085fad85ae473fbbcfce695d050dc2", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "7818182c65624d0f8d94471d24684f68": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_56261b59834f402d86eb8a3bcdae35d9", "IPY_MODEL_706174f71ed04da59539226646743f27" ], "layout": "IPY_MODEL_011f9537a8584b1f818c73bd4f1dedf8" } }, "7855f691fd2d417fb13802123060f0f9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_0c5d2aa95978492b96a43dd491bb1ce0", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "78b68e6794c840619e4f1949ebeace98": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_7be19293f8264133af3b1216e3f0a010", "IPY_MODEL_73a2a103d75245dfb95b7c238620db6e", "IPY_MODEL_d6baaeeb0c864af38c19897ffc3f5189" ], "layout": "IPY_MODEL_11713b7e91d44480ae51c2e032c722f2", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "7921b054744a46839a97eb1cff3c0d4d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7967dc587bfc4f6a929cd6958633f189": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "79f9cc5c06fb4eba9ba0de8566cdf87a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_0192e057585f45b2bc91c03e163d744f", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "7b0b2bd0a9fa41339eb0b6c7dc605df2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_97a68d30572c439c91a4a9a633b181ea", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "7be19293f8264133af3b1216e3f0a010": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f85df88b19a548c1a10678b27bff5d50", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "7bec16f4b1874da8953d4f5e66dca55e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_de540a7ac8634e82ac874e14cb8ee255", "IPY_MODEL_4c156340353a448aa26c07d00f3534ee", "IPY_MODEL_4348de50280a44f8a13045980f827e59" ], "layout": "IPY_MODEL_739b133f251f4d3bacfeda8ad058349a", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "7bf208ab93ca491faa2b1a31d26647b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7c3f6d4e80634978b877f69ba9e8e026": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7cc4ccb8773d4964a782cb362bbc0ccd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7e2618f90e4841b4bb23eed22a262b27": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_386e20fbf47c415bb2b40866aca1044d", "IPY_MODEL_4451e043cc5a4137b2ee99911d110707" ], "layout": "IPY_MODEL_fde5fa1711764a049b5bfa5f32acc020" } }, "7e414c835aa845c8959bb9cad0ac41f7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7ec48b24760846c183e6c0f90a972c7c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "805b154232864da38d1c482e12647dc1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "81cbd34f476d414db06011869f6c9433": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "82d1726ef6294fae9d9035a2eb7a40d7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_62a01d8f832140b98d3ff6af30de8e5b", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1aeroplane
2bicycle
3bird
4boat
5bottle
6bus
7car
8cat
9chair
10cow
11diningtable
12dog
13horse
14motorbike
15person
16pottedplant
17sheep
18sofa
19train
20tvmonitor
\n
", "text/plain": " category string\ncategory_id \n1 aeroplane\n2 bicycle\n3 bird\n4 boat\n5 bottle\n6 bus\n7 car\n8 cat\n9 chair\n10 cow\n11 diningtable\n12 dog\n13 horse\n14 motorbike\n15 person\n16 pottedplant\n17 sheep\n18 sofa\n19 train\n20 tvmonitor" }, "metadata": {}, "output_type": "display_data" } ] } }, "83026b8c3977479cb2153844b6328384": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "839b049fb7f1415da488222917572091": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_64846a560b0a4f4ebdd1e0f811fa78f0", "IPY_MODEL_61e34fb2b60a401785348f10aff29d49" ], "layout": "IPY_MODEL_3ab115aa46224d2a85191285da2cb442" } }, "839cd65655db4ceda9d80564d684e040": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8434e5f7d00a4bd8a08920774d7cb8d9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_d38bd83f704041fa94c4b2e8a381b218", "IPY_MODEL_f8fae047eba64b2ba615717c3e6424da" ], "layout": "IPY_MODEL_20e1a2357cb34893b0a962c96485a5e7" } }, "85980c920f294c12bb127a444d35c92b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "85f426e2ee5c45d5bec8cd768186b99c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "87fabb6909cd4796a02287f7620b30bd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_df046193ac084c8c98cdfa41744e9f6a", "IPY_MODEL_1ca7cc3b062b49f99c27a62e8fda36e5" ], "layout": "IPY_MODEL_8e4b95ada6b24c6da5ba4de9b630b66d" } }, "887db8657dbf4b9182d34277082dfbeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "891e741c61ef4eee9c8edaf8db76710b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_1015c44d1eff42be909b27d029faedab", "IPY_MODEL_2960d6dcc47d49a381e77ae2aa56613d" ], "layout": "IPY_MODEL_7cc4ccb8773d4964a782cb362bbc0ccd" } }, "893459f11983415abc20160387905a2a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "894942382e4348e19192c4e35fff114f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_357957009e864da7ae317388a2ca3d36", "IPY_MODEL_8be7cb103e1a48c4a91413508ca6d2ec" ], "layout": "IPY_MODEL_42f49954f3374d9db6e010ab6ad3e6cd" } }, "899cfc3a72f241b29a91abc01b84aee7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "8a0098265e3d4d468308cb112570d1eb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_dba7cb9df18749acb55c70e3e98d0170", "style": "IPY_MODEL_805b154232864da38d1c482e12647dc1", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "8b2d0ddb5a004197b4a18f8aae9de750": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_1d053d8ce8b24c67aa71cdd82d42465e", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1animal
2vehicle
3object
\n
", "text/plain": " category string\ncategory_id \n1 animal\n2 vehicle\n3 object" }, "metadata": {}, "output_type": "display_data" } ] } }, "8b7d15b97de0461e877c1b7f01376573": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8be7cb103e1a48c4a91413508ca6d2ec": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_138b3dfb7b344e32b7c97d0d52aad09a", "IPY_MODEL_25e0d0eb9c2c40488365ce8a27295bfc", "IPY_MODEL_82d1726ef6294fae9d9035a2eb7a40d7" ], "layout": "IPY_MODEL_97bc41e03f3746e697586d5eec134a1d", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "8c0b90b9d1204688990ab6844197f69e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8c824c73911a4b55afcd739b89c329be": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_fcae50c271324f3e885edb43b9eef38b", "IPY_MODEL_1439575ff6fb43709efa478aee4c5744", "IPY_MODEL_552731e964f94279afc56c9dcd2b189c" ], "layout": "IPY_MODEL_8fead985dfa34cb79a9ef802d118e5bc", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "8e1170f526454fa09c7b74e7eb8774df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_11d328b63a934f189ade88c99e48d93c", "IPY_MODEL_2d24a8f5bd594fc3a8d31f3474631e19" ], "layout": "IPY_MODEL_1ea85e14ffbd4a48aa549195fc5171ae" } }, "8e4b95ada6b24c6da5ba4de9b630b66d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8fead985dfa34cb79a9ef802d118e5bc": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8ff53f529b73486a9ef4acaf1c15d27f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_1e377c71b8164868a36e6a22cd9f1980", "IPY_MODEL_0d15ad48494c4511b4e2a3593479f44d" ], "layout": "IPY_MODEL_2a9067f1f71a45dea576b74070e26373" } }, "91e96815232c44b5835ed421e1516dca": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "945a36e7a2b34d4f933d295211f919ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "95264771ec314fe8ae75a4fc201f81db": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_4f56193640fa4ba2aad64ba1d62e31f1", "style": "IPY_MODEL_f8f81c3b192844218d8f0deddc1a2ade", "value": "

Dataset object containing 5,000 images and 20,607 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "9651979ed60141c0986497af84dfbab6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_a766c156b2cf489b8755aecda8252890", "style": "IPY_MODEL_cdeae99bee7c45338863518450b71d07", "value": "

Dataset object containing 5,000 images and 18,391 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "9658048ab1c54ee9a7f5c5394a33dc7b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "96b0b9ebdf5d49278fcc636d5fc1bdb3": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f3b91b3242c840a7891a3d9c2f89fb51", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "97a68d30572c439c91a4a9a633b181ea": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "97bc41e03f3746e697586d5eec134a1d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9892b77e263a4a86a7d43102ddf3268f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "98dc851e52e94d1682bf1c559530db18": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "98e9c540954743faacdc7b8ddea8124a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_0950d27d57f14519bb7d47741f951050", "style": "IPY_MODEL_612ca0d15721443686599d046a2aae92", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "9ab1868fedf14174836eee31d5aefa66": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "9b15214c36064511b4f73ebeb3398915": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9bd2a2ec87cd4d1cbe44151178f04c26": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_b3d6201c37db428ca755b1d1ad26a7a4", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
04999refrigerator82valid66.0094.8771.25194.2612029.44125
11person1valid112.43195.32214.78438.1948685.67910
21person1valid0.00256.0080.54376.8122650.73800
31frisbee34valid171.63424.0385.8940.672605.72090
42zebra24valid260.99158.88141.52194.119978.94125
..............................
367764999refrigerator82valid14.97104.0262.11166.128470.60700
367774999refrigerator82valid138.0062.6398.25252.0023037.46875
367784999refrigerator82valid234.4429.87113.49298.6530406.51295
367794999refrigerator82valid335.0410.48125.17318.7837187.97840
367804999refrigerator82valid460.390.0039.61328.6412492.52165
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 4999 refrigerator 82 valid 66.00 94.87 \n1 1 person 1 valid 112.43 195.32 \n2 1 person 1 valid 0.00 256.00 \n3 1 frisbee 34 valid 171.63 424.03 \n4 2 zebra 24 valid 260.99 158.88 \n... ... ... ... ... ... ... \n36776 4999 refrigerator 82 valid 14.97 104.02 \n36777 4999 refrigerator 82 valid 138.00 62.63 \n36778 4999 refrigerator 82 valid 234.44 29.87 \n36779 4999 refrigerator 82 valid 335.04 10.48 \n36780 4999 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n0 71.25 194.26 12029.44125 \n1 214.78 438.19 48685.67910 \n2 80.54 376.81 22650.73800 \n3 85.89 40.67 2605.72090 \n4 141.52 194.11 9978.94125 \n... ... ... ... \n36776 62.11 166.12 8470.60700 \n36777 98.25 252.00 23037.46875 \n36778 113.49 298.65 30406.51295 \n36779 125.17 318.78 37187.97840 \n36780 39.61 328.64 12492.52165 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "9beaa6e96d8c404f80fdd3c9ddcfb569": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_3b12a2ff1760404eb9d0f402e98dcd25", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
535917352582person1valid0.00256.0080.54376.8122650.73800
602093352582frisbee34valid171.63424.0385.8940.672605.72090
46790558393person1valid342.52163.33192.2477.475408.64720
57690058393bench15valid44.78242.27547.16224.9882291.54995
..............................
109094428280chair62valid187.71114.8044.0766.412069.93800
578099428280bench15valid0.75184.42181.43132.9013841.81490
1101888428280laptop73valid118.71138.3444.7935.831405.98065
1117514428280keyboard76valid121.04165.6540.276.02217.08440
2190513428280chair62valid203.58179.65105.27139.557696.12525
\n

18670 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n467905 58393 person 1 valid 342.52 163.33 \n576900 58393 bench 15 valid 44.78 242.27 \n... ... ... ... ... ... ... \n109094 428280 chair 62 valid 187.71 114.80 \n578099 428280 bench 15 valid 0.75 184.42 \n1101888 428280 laptop 73 valid 118.71 138.34 \n1117514 428280 keyboard 76 valid 121.04 165.65 \n2190513 428280 chair 62 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n467905 192.24 77.47 5408.64720 \n576900 547.16 224.98 82291.54995 \n... ... ... ... \n109094 44.07 66.41 2069.93800 \n578099 181.43 132.90 13841.81490 \n1101888 44.79 35.83 1405.98065 \n1117514 40.27 6.02 217.08440 \n2190513 105.27 139.55 7696.12525 \n\n[18670 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "9c276f07f9a94174b46eb4a8e4ef66e8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_9651979ed60141c0986497af84dfbab6", "IPY_MODEL_ecd7d7225e2840839e4dd5c06d2928ac" ], "layout": "IPY_MODEL_8c0b90b9d1204688990ab6844197f69e" } }, "9c627853f4364595b494460cc3f26a45": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9cee0d099ede47c38f043758df377b04": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_a1a934c35f1744738ff877238831cf4a", "IPY_MODEL_65527049e5a7455e9598477f43a0cfd2" ], "layout": "IPY_MODEL_648ed8ee168c4e6a839ca4bbebcab214" } }, "9d15ec8503094cc2b2668aaeacb22cb8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9d1b24a324584fc58c0802d2c2705993": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9e5e24ac2e3c472fa147368ac95fe73b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9f7b0de8a9b044f0a71fb2f1470c8821": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_d9c58b82617642ef812454748838b5f1", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "9fe2579380ec4ab2afa63f5f450a27b6": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_301211805d4c40f580e4d61f9e359404", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "a0530d16806447fb887a285e3298f421": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a1a934c35f1744738ff877238831cf4a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_7ec48b24760846c183e6c0f90a972c7c", "style": "IPY_MODEL_31c5d5f134534aa2b95fe24095c20404", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "a280778b18634b7dbfefbd13f3430577": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "a31aecdc64ab40a5ba5b7779bcf4a93b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a3ccb2ad017443f2b271f9140538e20a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a766c156b2cf489b8755aecda8252890": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "a76843a616534c5a84fda9a53ae68fc7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f2276d30d3b548dbbec09aca7a954f7b", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582object3valid112.43195.32214.78438.1948685.67910
535917352582object3valid0.00256.0080.54376.8122650.73800
46790558393object3valid342.52163.33192.2477.475408.64720
171099058393object3valid418.99182.6561.1245.001792.80770
1238519147729object3valid0.0087.01310.67287.9955847.52705
..............................
1192747105455vehicle2valid333.56517.2117.4314.58182.21680
108177428280object3valid3.58189.14176.88125.329942.25095
108343428280object3valid244.05152.2968.5134.491457.96785
109094428280object3valid187.71114.8044.0766.412069.93800
2190513428280object3valid203.58179.65105.27139.557696.12525
\n

20607 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 object 3 valid 112.43 195.32 \n535917 352582 object 3 valid 0.00 256.00 \n467905 58393 object 3 valid 342.52 163.33 \n1710990 58393 object 3 valid 418.99 182.65 \n1238519 147729 object 3 valid 0.00 87.01 \n... ... ... ... ... ... ... \n1192747 105455 vehicle 2 valid 333.56 517.21 \n108177 428280 object 3 valid 3.58 189.14 \n108343 428280 object 3 valid 244.05 152.29 \n109094 428280 object 3 valid 187.71 114.80 \n2190513 428280 object 3 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n1238519 310.67 287.99 55847.52705 \n... ... ... ... \n1192747 17.43 14.58 182.21680 \n108177 176.88 125.32 9942.25095 \n108343 68.51 34.49 1457.96785 \n109094 44.07 66.41 2069.93800 \n2190513 105.27 139.55 7696.12525 \n\n[20607 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "a799764dd9924208b78132c873ae60c0": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_5bd0b17b9d4e4008939ba492c6a5377a", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "a92f13c01f914b8496025488730f1f55": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_d9b40f57cb824239b65cd0e1c09fc5c6", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1Animal
2Vehicle
3Object
\n
", "text/plain": " category string\ncategory_id \n1 Animal\n2 Vehicle\n3 Object" }, "metadata": {}, "output_type": "display_data" } ] } }, "a9f2687fd4be4fb5b68c5c24c5dcca30": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_95264771ec314fe8ae75a4fc201f81db", "IPY_MODEL_bb79da4e6a5746d8b82f1ea4709f1076" ], "layout": "IPY_MODEL_2cf5d9a88c3247949c850946da75ea68" } }, "aab9806ceeab454f9401385c09ef3e1a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_eb29c47be0b14b9dbbeebf180e3c7c02", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
0640486Images/valid/000000058393.jpg.jpgvalid
1425640Images/valid/000000352582.jpg.jpgvalid
2640480Images/valid/000000113354.jpg.jpgvalid
3500375Images/valid/000000147729.jpg.jpgvalid
4640383Images/valid/000000310072.jpg.jpgvalid
..................
4995480640Images/valid/000000311180.jpg.jpgvalid
4996640359Images/valid/000000302030.jpg.jpgvalid
4997427640Images/valid/000000105455.jpg.jpgvalid
4998500333Images/valid/000000428280.jpg.jpgvalid
4999500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n0 640 486 Images/valid/000000058393.jpg .jpg valid\n1 425 640 Images/valid/000000352582.jpg .jpg valid\n2 640 480 Images/valid/000000113354.jpg .jpg valid\n3 500 375 Images/valid/000000147729.jpg .jpg valid\n4 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n4995 480 640 Images/valid/000000311180.jpg .jpg valid\n4996 640 359 Images/valid/000000302030.jpg .jpg valid\n4997 427 640 Images/valid/000000105455.jpg .jpg valid\n4998 500 333 Images/valid/000000428280.jpg .jpg valid\n4999 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "aae989eac45f42fcb39884378a59e020": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_10389948eb0448bfb97290f8ee6e4654", "style": "IPY_MODEL_9658048ab1c54ee9a7f5c5394a33dc7b", "value": "

Dataset object containing 4,706 images and 18,391 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "aaf31450ec344590a7e1bc8e6c8260ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_bb6a36c9e5c746dda3420c6d73a19013", "style": "IPY_MODEL_cc19a92483ae462d8c39de44985974e6", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "ac087e9ac3c140c79ea14a6e6aaffbb7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_4fe4608ef6e34b44a3d0cd32ccd2b5b1", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1aeroplane
2bicycle
3bird
4boat
5bottle
6bus
7car
8cat
9chair
10cow
11diningtable
12dog
13horse
14motorbike
15person
16pottedplant
17sheep
18sofa
19train
20tvmonitor
\n
", "text/plain": " category string\ncategory_id \n1 aeroplane\n2 bicycle\n3 bird\n4 boat\n5 bottle\n6 bus\n7 car\n8 cat\n9 chair\n10 cow\n11 diningtable\n12 dog\n13 horse\n14 motorbike\n15 person\n16 pottedplant\n17 sheep\n18 sofa\n19 train\n20 tvmonitor" }, "metadata": {}, "output_type": "display_data" } ] } }, "adb47fe414d54c978a5f98dd7a48f48e": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_e7dc427815dc4f14ba367e6f6e37a129", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
447789640427Images/valid/000000447789.jpg.jpgvalid
514540429640Images/valid/000000514540.jpg.jpgvalid
476491336500Images/valid/000000476491.jpg.jpgvalid
41488640369Images/valid/000000041488.jpg.jpgvalid
121153640480Images/valid/000000121153.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

4706 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n447789 640 427 Images/valid/000000447789.jpg .jpg valid\n514540 429 640 Images/valid/000000514540.jpg .jpg valid\n476491 336 500 Images/valid/000000476491.jpg .jpg valid\n41488 640 369 Images/valid/000000041488.jpg .jpg valid\n121153 640 480 Images/valid/000000121153.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[4706 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "aea9b77bd8d14a21aba81dc81fe0e8cd": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_d9593d212484414b8ba57837f931a0f8", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid" }, "metadata": {}, "output_type": "display_data" } ] } }, "afb1b69762974288866c8d07a7ccc0cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b189ddb82297439bb62349d10e0b8c8c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b25e07525da2464e96bc0df015a82213": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_fcff304526f24f9b8d49b5adc8dfe9a7", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightareaconfidence
id
0352582tvmonitor20valid212.5320.0212.5320.068000.00.5
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 352582 tvmonitor 20 valid 212.5 320.0 \n\n box_width box_height area confidence \nid \n0 212.5 320.0 68000.0 0.5 " }, "metadata": {}, "output_type": "display_data" } ] } }, "b2c77bc7409b41af9dbe153b7d7df4da": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f1a4502ebbd14377b5b5f6c34f45482f", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
592005113354zebra24valid3.24151.28265.34175.8216206.37480
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n589077 113354 zebra 24 valid 260.99 158.88 \n589740 113354 zebra 24 valid 366.49 174.59 \n592005 113354 zebra 24 valid 3.24 151.28 \n\n box_width box_height area \nid \n589077 141.52 194.11 9978.94125 \n589740 115.67 142.71 5784.68620 \n592005 265.34 175.82 16206.37480 " }, "metadata": {}, "output_type": "display_data" } ] } }, "b39364cbbf6b4a91af4b560782c16676": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_9e5e24ac2e3c472fa147368ac95fe73b", "style": "IPY_MODEL_a280778b18634b7dbfefbd13f3430577", "value": "

Dataset object containing 5,000 images and 18,391 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "b3d6201c37db428ca755b1d1ad26a7a4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b5454727f531449daeee78ba0af3a2ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b60cb52cd3e4493283ae46259ea964f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b75cc350015f4ed68787675876e5fc41": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b7a7836c0928481eb773f233fab0a635": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "b9305f4cb6694a218d938b1c821d0423": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bb6a36c9e5c746dda3420c6d73a19013": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bb79da4e6a5746d8b82f1ea4709f1076": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_bc1651b6de75415c95fdffbbd183db65", "IPY_MODEL_cbfbf0e7eb0d4637b841fa079d924fd0", "IPY_MODEL_8b2d0ddb5a004197b4a18f8aae9de750" ], "layout": "IPY_MODEL_1b66cc1c194b4bb1b4de420e94dd4e1a", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "bc1651b6de75415c95fdffbbd183db65": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_85980c920f294c12bb127a444d35c92b", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "bc33b1752fbc42b1bb971160aa734c1f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bc405de6314442108a926ea56be3411f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bd34f01e287a47d79a85c4dfb923c687": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bf056b3aad2f4866a73886e0a2548fc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "bf859b3043c14f999f630d7d9657d4b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_33e8c53a668c4f61bfde88a0a536ea24", "IPY_MODEL_7624ce323ee04e1c9158288ba8c702ef", "IPY_MODEL_003973ba6429441696690892068b30b5" ], "layout": "IPY_MODEL_4af4df92ffd74f19a264c6298fec7326", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "c0010570c6a04f1b978cf99e5d075b34": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c0624f6057fb4ff99ed6509a042305ab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_2a3ca1e55ef24e72b351e4d45e0d7d53", "IPY_MODEL_1d6d3b6ca031443a9d4de538dee1f120", "IPY_MODEL_7b0b2bd0a9fa41339eb0b6c7dc605df2" ], "layout": "IPY_MODEL_2315e499822d468c8925f1d42b700cf9", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "c1120547b03e442abc27bc1dd3d922d4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_4b5120a0e8274f3babc93743e1e22543", "IPY_MODEL_7bec16f4b1874da8953d4f5e66dca55e" ], "layout": "IPY_MODEL_0207c44a5d5142f5bdc2c9757b174ff4" } }, "c1569471bd79471f9f571bd1e87ccf78": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_3ea45c5b477b49308bb0e428284e33f5", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589740113354zebra24valid366.49174.59115.67142.715784.68620
46790558393person1valid342.52163.33192.2477.475408.64720
171099058393person1valid418.99182.6561.1245.001792.80770
..............................
1101888428280laptop73valid118.71138.3444.7935.831405.98065
2190513428280chair62valid203.58179.65105.27139.557696.12525
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

18391 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589740 113354 zebra 24 valid 366.49 174.59 \n467905 58393 person 1 valid 342.52 163.33 \n1710990 58393 person 1 valid 418.99 182.65 \n... ... ... ... ... ... ... \n1101888 428280 laptop 73 valid 118.71 138.34 \n2190513 428280 chair 62 valid 203.58 179.65 \n331107 349837 refrigerator 82 valid 66.00 94.87 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n602093 85.89 40.67 2605.72090 \n589740 115.67 142.71 5784.68620 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n... ... ... ... \n1101888 44.79 35.83 1405.98065 \n2190513 105.27 139.55 7696.12525 \n331107 71.25 194.26 12029.44125 \n333394 113.49 298.65 30406.51295 \n333731 39.61 328.64 12492.52165 \n\n[18391 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "c17e5469e14e45389ae5670a74d3fd6b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c1ee2b30e8c1430a91065ddc529d89e7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "c497c29f8fa5485fb878086fd794cf28": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c6a12a1635ad495cb5d87a4f8c586bfb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c6f7a9fda097487d8e8c50b0cacdf846": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_d7cdaeb49f004a8dbdf16961b178f497", "IPY_MODEL_cc4cba889012456087f1096113d0d4a9", "IPY_MODEL_2eda4e5db5ac43819b8bbcb8291987d2" ], "layout": "IPY_MODEL_9b15214c36064511b4f73ebeb3398915", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "c74870f966e84cdf8b0134024009119e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c7a7060a8aa7446299af9cc8d1cab19e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_839cd65655db4ceda9d80564d684e040", "style": "IPY_MODEL_fbeaaa18eb984d3196b1f14067c573b2", "value": "

Dataset object containing 1 image and 3 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "c7aea4c13f6f4566a773c0c3cc2be09c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c7f4e636fea5416abe53471ffe768a14": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c986fa54a7fa47558c32767b5450a531": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_47f377ac62584f86894264ba0e7c570d", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "cb175f16ce6c4ed3963131b9bc9d71d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "cb80ecd92080438fa9609089ad11c3ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "cbfbf0e7eb0d4637b841fa079d924fd0": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_4e9e02be8d9f47a883d4b2e4c7dd3f5f", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582object3valid112.43195.32214.78438.1948685.67910
535917352582object3valid0.00256.0080.54376.8122650.73800
46790558393object3valid342.52163.33192.2477.475408.64720
171099058393object3valid418.99182.6561.1245.001792.80770
1238519147729object3valid0.0087.01310.67287.9955847.52705
..............................
1192747105455vehicle2valid333.56517.2117.4314.58182.21680
108177428280object3valid3.58189.14176.88125.329942.25095
108343428280object3valid244.05152.2968.5134.491457.96785
109094428280object3valid187.71114.8044.0766.412069.93800
2190513428280object3valid203.58179.65105.27139.557696.12525
\n

20607 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 object 3 valid 112.43 195.32 \n535917 352582 object 3 valid 0.00 256.00 \n467905 58393 object 3 valid 342.52 163.33 \n1710990 58393 object 3 valid 418.99 182.65 \n1238519 147729 object 3 valid 0.00 87.01 \n... ... ... ... ... ... ... \n1192747 105455 vehicle 2 valid 333.56 517.21 \n108177 428280 object 3 valid 3.58 189.14 \n108343 428280 object 3 valid 244.05 152.29 \n109094 428280 object 3 valid 187.71 114.80 \n2190513 428280 object 3 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n1238519 310.67 287.99 55847.52705 \n... ... ... ... \n1192747 17.43 14.58 182.21680 \n108177 176.88 125.32 9942.25095 \n108343 68.51 34.49 1457.96785 \n109094 44.07 66.41 2069.93800 \n2190513 105.27 139.55 7696.12525 \n\n[20607 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "cc19a92483ae462d8c39de44985974e6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "cc4cba889012456087f1096113d0d4a9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_83026b8c3977479cb2153844b6328384", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.67910
535917352582person1valid0.00256.0080.54376.8122650.73800
602093352582frisbee34valid171.63424.0385.8940.672605.72090
589077113354zebra24valid260.99158.88141.52194.119978.94125
589740113354zebra24valid366.49174.59115.67142.715784.68620
..............................
331107349837refrigerator82valid66.0094.8771.25194.2612029.44125
332788349837refrigerator82valid138.0062.6398.25252.0023037.46875
333394349837refrigerator82valid234.4429.87113.49298.6530406.51295
333685349837refrigerator82valid335.0410.48125.17318.7837187.97840
333731349837refrigerator82valid460.390.0039.61328.6412492.52165
\n

36781 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n589077 113354 zebra 24 valid 260.99 158.88 \n589740 113354 zebra 24 valid 366.49 174.59 \n... ... ... ... ... ... ... \n331107 349837 refrigerator 82 valid 66.00 94.87 \n332788 349837 refrigerator 82 valid 138.00 62.63 \n333394 349837 refrigerator 82 valid 234.44 29.87 \n333685 349837 refrigerator 82 valid 335.04 10.48 \n333731 349837 refrigerator 82 valid 460.39 0.00 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n602093 85.89 40.67 2605.72090 \n589077 141.52 194.11 9978.94125 \n589740 115.67 142.71 5784.68620 \n... ... ... ... \n331107 71.25 194.26 12029.44125 \n332788 98.25 252.00 23037.46875 \n333394 113.49 298.65 30406.51295 \n333685 125.17 318.78 37187.97840 \n333731 39.61 328.64 12492.52165 \n\n[36781 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "cc92718aa17c4fdc9b519e75d09e7773": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ccbe6d78c4684bbe927f1efdbdc0f52d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_33dd6b044ff448fb8de7b1e2d0f8cf58", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person15valid112.43195.32214.78438.1948685.67910
535917352582person15valid0.00256.0080.54376.8122650.73800
46790558393person15valid342.52163.33192.2477.475408.64720
171099058393person15valid418.99182.6561.1245.001792.80770
1238519147729person15valid0.0087.01310.67287.9955847.52705
..............................
906701608203105455car7valid333.56517.2117.4314.58182.21680
906700523633428280chair9valid3.58189.14176.88125.329942.25095
906700523799428280chair9valid244.05152.2968.5134.491457.96785
906700524550428280chair9valid187.71114.8044.0766.412069.93800
906702605969428280chair9valid203.58179.65105.27139.557696.12525
\n

41900 rows × 9 columns

\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 15 valid 112.43 195.32 \n535917 352582 person 15 valid 0.00 256.00 \n467905 58393 person 15 valid 342.52 163.33 \n1710990 58393 person 15 valid 418.99 182.65 \n1238519 147729 person 15 valid 0.00 87.01 \n... ... ... ... ... ... ... \n906701608203 105455 car 7 valid 333.56 517.21 \n906700523633 428280 chair 9 valid 3.58 189.14 \n906700523799 428280 chair 9 valid 244.05 152.29 \n906700524550 428280 chair 9 valid 187.71 114.80 \n906702605969 428280 chair 9 valid 203.58 179.65 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.67910 \n535917 80.54 376.81 22650.73800 \n467905 192.24 77.47 5408.64720 \n1710990 61.12 45.00 1792.80770 \n1238519 310.67 287.99 55847.52705 \n... ... ... ... \n906701608203 17.43 14.58 182.21680 \n906700523633 176.88 125.32 9942.25095 \n906700523799 68.51 34.49 1457.96785 \n906700524550 44.07 66.41 2069.93800 \n906702605969 105.27 139.55 7696.12525 \n\n[41900 rows x 9 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "ccc69999bf004cdcbde3354d74c78850": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_cb175f16ce6c4ed3963131b9bc9d71d4", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
460450352582person1valid112.43195.32214.78438.1948685.6791
535917352582person1valid0.00256.0080.54376.8122650.7380
602093352582frisbee34valid171.63424.0385.8940.672605.7209
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n460450 352582 person 1 valid 112.43 195.32 \n535917 352582 person 1 valid 0.00 256.00 \n602093 352582 frisbee 34 valid 171.63 424.03 \n\n box_width box_height area \nid \n460450 214.78 438.19 48685.6791 \n535917 80.54 376.81 22650.7380 \n602093 85.89 40.67 2605.7209 " }, "metadata": {}, "output_type": "display_data" } ] } }, "ccd0c39ae0d646ff9987e51710d01995": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "cda0463595624e2b80fb4a544b051fe8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_9fe2579380ec4ab2afa63f5f450a27b6", "IPY_MODEL_215230f60dc54b66975684b3962c0042", "IPY_MODEL_c986fa54a7fa47558c32767b5450a531" ], "layout": "IPY_MODEL_196967d5069f4d189f12628fb951211b", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "cdeae99bee7c45338863518450b71d07": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "d2a60949036846628bae224850cb21e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_7855f691fd2d417fb13802123060f0f9", "IPY_MODEL_51b02e3991f24fd6b09d0dbe65efc24f", "IPY_MODEL_a92f13c01f914b8496025488730f1f55" ], "layout": "IPY_MODEL_ee65a2fc04d248e18e8798ea317f9557", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "d38bd83f704041fa94c4b2e8a381b218": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_dadb0f6788394aa0a94d6c7588d79522", "style": "IPY_MODEL_03a9542f0e794f079aad4ea10017013f", "value": "

Dataset object containing 2 images and 2 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "d5e3a1dbf17e4f24bbac1e79e3ccea3a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_8b7d15b97de0461e877c1b7f01376573", "style": "IPY_MODEL_c1ee2b30e8c1430a91065ddc529d89e7", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "d6baaeeb0c864af38c19897ffc3f5189": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_cc92718aa17c4fdc9b519e75d09e7773", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "d6fe810a673b4df3b2dee940a9ca2897": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "d7cdaeb49f004a8dbdf16961b178f497": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_24c3935dccad491d92f92a9a01fa5f37", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "d89a9273a3264bdc8aaeac981e15bd3b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "d8da17be643c4a63813fb457a14bd259": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7c3f6d4e80634978b877f69ba9e8e026", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
\n
", "text/plain": "Empty DataFrame\nColumns: [image_id, category_str, category_id, split, box_x_min, box_y_min, box_width, box_height, area]\nIndex: []" }, "metadata": {}, "output_type": "display_data" } ] } }, "d9593d212484414b8ba57837f931a0f8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "d9b40f57cb824239b65cd0e1c09fc5c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "d9c58b82617642ef812454748838b5f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dabce91a4a4b454dabeb9c3a486c0d43": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_b189ddb82297439bb62349d10e0b8c8c", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "dadb0f6788394aa0a94d6c7588d79522": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "db244fd778374cd9a420b62f45288859": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dba7cb9df18749acb55c70e3e98d0170": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "dd35663bc1bb459985d75e20a0f2885d": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "de540a7ac8634e82ac874e14cb8ee255": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_bf056b3aad2f4866a73886e0a2548fc2", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "deed7abbfb4942c1bc39b66fb3b7efe4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "df046193ac084c8c98cdfa41744e9f6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_63b646f5fe4b44e5a9eb540679b38179", "style": "IPY_MODEL_3209edc462354a22a7d772253934a13c", "value": "

Dataset object containing 5,000 images and 36,781 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "df16b3e64151406f8c3c36f3c8b766ff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_b39364cbbf6b4a91af4b560782c16676", "IPY_MODEL_78b68e6794c840619e4f1949ebeace98" ], "layout": "IPY_MODEL_160d21b7b91544a69bd0ec984366f70e" } }, "e10607eb5ef04569a4c9a797967b2da8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e2d3753a75aa4fb1924f269175afeeab": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e345619f1ff841aeb526c74d4a9afa99": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e7dc427815dc4f14ba367e6f6e37a129": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e7ea818c8348466b9b548a435e1af377": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "e82f91fa62f44f708868b2c85acde7b1": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ea17d1aba9cb4d4cac2d795b5295c04c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ea276594fe214985ae130d548ca3874a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ea3aef7e74fd4993a10850c12aa9ef62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_f9cc155120424502aaf9267336ce1ae1", "IPY_MODEL_550e373b38a54c63974397f70115ae35" ], "layout": "IPY_MODEL_5fb86192340c4526a85900dff25de564" } }, "ea8cea5708c2485c9e3df8088c556f5c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_b5454727f531449daeee78ba0af3a2ec", "style": "IPY_MODEL_fec8054bf05b46a3a571f7d52a80ea4b", "value": "

Dataset object containing 1 image and 3 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "eb29c47be0b14b9dbbeebf180e3c7c02": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "ec10a02b68d543e2a7202f4f193def5c": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_a31aecdc64ab40a5ba5b7779bcf4a93b", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1person
2bicycle
3car
4motorcycle
5airplane
......
86vase
87scissors
88teddy bear
89hair drier
90toothbrush
\n

80 rows × 1 columns

\n
", "text/plain": " category string\ncategory_id \n1 person\n2 bicycle\n3 car\n4 motorcycle\n5 airplane\n... ...\n86 vase\n87 scissors\n88 teddy bear\n89 hair drier\n90 toothbrush\n\n[80 rows x 1 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "ecc7c3f906844b789a62761ee82aeaa9": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_6ec0501159b94ca7aaeac2588c2e0a9e", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

4722 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[4722 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "ecd7d7225e2840839e4dd5c06d2928ac": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_f5bc91bf4a9a43e085d27ce6c37f462a", "IPY_MODEL_1ffadd66d4824428bf4bd02321a8baf0", "IPY_MODEL_2d7db77130b54cafb143a1b7c507fa71" ], "layout": "IPY_MODEL_f7009f2f215643cb9acae7c654699e36", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "ecf576b29a014db481e9829081b09c68": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_bc405de6314442108a926ea56be3411f", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
10640426Images/valid/000000000139.jpg.jpgvalid
11586640Images/valid/000000000285.jpg.jpgvalid
12640483Images/valid/000000000632.jpg.jpgvalid
13375500Images/valid/000000000724.jpg.jpgvalid
14428640Images/valid/000000000776.jpg.jpgvalid
..................
5005640354Images/valid/000000581317.jpg.jpgvalid
5006612612Images/valid/000000581357.jpg.jpgvalid
5007640427Images/valid/000000581482.jpg.jpgvalid
5008478640Images/valid/000000581615.jpg.jpgvalid
5009640478Images/valid/000000581781.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n10 640 426 Images/valid/000000000139.jpg .jpg valid\n11 586 640 Images/valid/000000000285.jpg .jpg valid\n12 640 483 Images/valid/000000000632.jpg .jpg valid\n13 375 500 Images/valid/000000000724.jpg .jpg valid\n14 428 640 Images/valid/000000000776.jpg .jpg valid\n... ... ... ... ... ...\n5005 640 354 Images/valid/000000581317.jpg .jpg valid\n5006 612 612 Images/valid/000000581357.jpg .jpg valid\n5007 640 427 Images/valid/000000581482.jpg .jpg valid\n5008 478 640 Images/valid/000000581615.jpg .jpg valid\n5009 640 478 Images/valid/000000581781.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "ee65a2fc04d248e18e8798ea317f9557": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f1a4502ebbd14377b5b5f6c34f45482f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f1b510f4d8bb4bba8ad06e2e2a53619f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f2276d30d3b548dbbec09aca7a954f7b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f288611e5cda425387ea6350e30e1af2": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7967dc587bfc4f6a929cd6958633f189", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid" }, "metadata": {}, "output_type": "display_data" } ] } }, "f29e02fa480a4e51b63c85f5a07560dc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "f2c0045fe58b43f59f4729a0edddcbdd": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f32c7f63262042e18b56852b902d40c0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f3b91b3242c840a7891a3d9c2f89fb51": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f5bc91bf4a9a43e085d27ce6c37f462a": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_faba574fc64a47609236f420e3c1a5be", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid\n58393 640 486 Images/valid/000000058393.jpg .jpg valid\n147729 500 375 Images/valid/000000147729.jpg .jpg valid\n310072 640 383 Images/valid/000000310072.jpg .jpg valid\n... ... ... ... ... ...\n311180 480 640 Images/valid/000000311180.jpg .jpg valid\n302030 640 359 Images/valid/000000302030.jpg .jpg valid\n105455 427 640 Images/valid/000000105455.jpg .jpg valid\n428280 500 333 Images/valid/000000428280.jpg .jpg valid\n349837 500 333 Images/valid/000000349837.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "f5df0a434c4f4e739d799df0d6930bf8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f61c9d7c59b849afb523a7044b316296": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_29c931608dbf4121b1cbb276ae817e39", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1aeroplane
2bicycle
3bird
4boat
5bottle
6bus
7car
8cat
9chair
10cow
11diningtable
12dog
13horse
14motorbike
15person
16pottedplant
17sheep
18sofa
19train
20tvmonitor
\n
", "text/plain": " category string\ncategory_id \n1 aeroplane\n2 bicycle\n3 bird\n4 boat\n5 bottle\n6 bus\n7 car\n8 cat\n9 chair\n10 cow\n11 diningtable\n12 dog\n13 horse\n14 motorbike\n15 person\n16 pottedplant\n17 sheep\n18 sofa\n19 train\n20 tvmonitor" }, "metadata": {}, "output_type": "display_data" } ] } }, "f628d827f3494e61a5e13c72567f8983": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7921b054744a46839a97eb1cff3c0d4d", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
category string
category_id
1animal
2vehicle
3object
\n
", "text/plain": " category string\ncategory_id \n1 animal\n2 vehicle\n3 object" }, "metadata": {}, "output_type": "display_data" } ] } }, "f7009f2f215643cb9acae7c654699e36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f7fb857318714b95b6a4cadc2be3f42c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f85df88b19a548c1a10678b27bff5d50": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f8bb9c3a15c947268a2a91f733da3611": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "f8f81c3b192844218d8f0deddc1a2ade": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "f8fae047eba64b2ba615717c3e6424da": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_fb7106a5a3a8416ba13d80976377be51", "IPY_MODEL_fd96c177ea0f47a7b9d0151b6a44e3d3", "IPY_MODEL_63d68d45e3cc4478ac890817f2e48433" ], "layout": "IPY_MODEL_bc33b1752fbc42b1bb971160aa734c1f", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "f9cc155120424502aaf9267336ce1ae1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_494d15aa83c24127ac87b30c627df20b", "style": "IPY_MODEL_68d2c10291ca463ab2cf05746ca09093", "value": "

Dataset object containing 4,722 images and 34,818 objects\nName :\n\tcoco\nImages root :\n\tnotebook_data

" } }, "fa468d8ea1954346ba62e1f3ba16da9d": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_31e84d0a79824ce9a92d05234d3306bf", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
0640483Images/valid/000000000632.jpg.jpgvalid
1375500Images/valid/000000000724.jpg.jpgvalid
2428640Images/valid/000000000776.jpg.jpgvalid
3640425Images/valid/000000000785.jpg.jpgvalid
4640427Images/valid/000000001268.jpg.jpgvalid
..................
4995640428Images/valid/000000580418.jpg.jpgvalid
4996640425Images/valid/000000580757.jpg.jpgvalid
4997500375Images/valid/000000581062.jpg.jpgvalid
4998479640Images/valid/000000581206.jpg.jpgvalid
4999478640Images/valid/000000581615.jpg.jpgvalid
\n

5000 rows × 5 columns

\n
", "text/plain": " width height relative_path type split\nid \n0 640 483 Images/valid/000000000632.jpg .jpg valid\n1 375 500 Images/valid/000000000724.jpg .jpg valid\n2 428 640 Images/valid/000000000776.jpg .jpg valid\n3 640 425 Images/valid/000000000785.jpg .jpg valid\n4 640 427 Images/valid/000000001268.jpg .jpg valid\n... ... ... ... ... ...\n4995 640 428 Images/valid/000000580418.jpg .jpg valid\n4996 640 425 Images/valid/000000580757.jpg .jpg valid\n4997 500 375 Images/valid/000000581062.jpg .jpg valid\n4998 479 640 Images/valid/000000581206.jpg .jpg valid\n4999 478 640 Images/valid/000000581615.jpg .jpg valid\n\n[5000 rows x 5 columns]" }, "metadata": {}, "output_type": "display_data" } ] } }, "faba574fc64a47609236f420e3c1a5be": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fb7106a5a3a8416ba13d80976377be51": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_b9305f4cb6694a218d938b1c821d0423", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
\n
", "text/plain": " width height relative_path type split\nid \n352582 425 640 Images/valid/000000352582.jpg .jpg valid\n113354 640 480 Images/valid/000000113354.jpg .jpg valid" }, "metadata": {}, "output_type": "display_data" } ] } }, "fbeaaa18eb984d3196b1f14067c573b2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "fbf95a22080b42069f45fa21cb13bec1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_aae989eac45f42fcb39884378a59e020", "IPY_MODEL_6d671c481a0946cd93a7b18099a60178" ], "layout": "IPY_MODEL_9d1b24a324584fc58c0802d2c2705993" } }, "fcae50c271324f3e885edb43b9eef38b": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_c7f4e636fea5416abe53471ffe768a14", "outputs": [ { "data": { "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
widthheightrelative_pathtypesplit
id
352582425640Images/valid/000000352582.jpg.jpgvalid
113354640480Images/valid/000000113354.jpg.jpgvalid
58393640486Images/valid/000000058393.jpg.jpgvalid
147729500375Images/valid/000000147729.jpg.jpgvalid
310072640383Images/valid/000000310072.jpg.jpgvalid
..................
311180480640Images/valid/000000311180.jpg.jpgvalid
302030640359Images/valid/000000302030.jpg.jpgvalid
105455427640Images/valid/000000105455.jpg.jpgvalid
428280500333Images/valid/000000428280.jpg.jpgvalid
349837500333Images/valid/000000349837.jpg.jpgvalid
\n

5000 rows × 5 columns

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\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightareaconfidence
id
0352582tvmonitor20valid212.5320.0212.5320.068000.00.5
11133542121valid0.00.0320.0240.076800.00.5
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 352582 tvmonitor 20 valid 212.5 320.0 \n1 113354 21 21 valid 0.0 0.0 \n\n box_width box_height area confidence \nid \n0 212.5 320.0 68000.0 0.5 \n1 320.0 240.0 76800.0 0.5 " }, "metadata": {}, "output_type": "display_data" } ] } }, "fde5fa1711764a049b5bfa5f32acc020": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "fec8054bf05b46a3a571f7d52a80ea4b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }