{ "cells": [ { "cell_type": "markdown", "id": "8dd43ef7", "metadata": {}, "source": [ "# Lours to Fiftyone conversion\n", "\n", "This notebook will make use of Lours's data object and convert it to fiftyone\n", "\n", ".. nbinfo::\n", " If you are looking at the documentation website, every time you see a `fo.launch_app`, you might see a either a lowres still image or a broken iframe, it's the fiftyone app client that is not working since the server no longer running. To see them working, you will have to run the notebook yourself." ] }, { "cell_type": "code", "execution_count": 1, "id": "a9865afa", "metadata": {}, "outputs": [], "source": [ "import fiftyone as fo\n", "\n", "from lours.dataset import Dataset, from_coco, from_coco_keypoints" ] }, { "cell_type": "markdown", "id": "360b5478", "metadata": {}, "source": [ "## Load a bounding boxes dataset in test folders." ] }, { "cell_type": "code", "execution_count": 2, "id": "62a029e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking Image and annotations Ids ...\n", "Checking Bounding boxes ..\n", "Checking label map ...\n", "Checking images are valid ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e2ff6e3043da4ac0a7c08a2dd415f62c", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset" ] }, { "cell_type": "markdown", "id": "76f2d8c6", "metadata": {}, "source": [ "Converting the dataset to fiftyone and launching the app" ] }, { "cell_type": "code", "execution_count": 4, "id": "8318c8fa", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7aabeb9d1114477385ed7ce69d39d72a", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\n", "@import url(\"https://fonts.googleapis.com/css2?family=Palanquin&display=swap\");\n", "\n", "body, html {\n", " margin: 0;\n", " padding: 0;\n", " width: 100%;\n", "}\n", "\n", "#focontainer-76833dd6-a721-43a6-a7e0-0049938076e3 {\n", " position: relative;\n", " height: 800px;\n", " display: block !important;\n", "}\n", "#foactivate-76833dd6-a721-43a6-a7e0-0049938076e3 {\n", " font-weight: bold;\n", " cursor: pointer;\n", " font-size: 24px;\n", " border-radius: 3px;\n", " text-align: center;\n", " padding: 0.5em;\n", " color: rgb(255, 255, 255);\n", " font-family: \"Palanquin\", sans-serif;\n", " position: absolute;\n", " left: 50%;\n", " top: 50%;\n", " width: 160px;\n", " margin-left: -80px;\n", " margin-top: -23px;\n", " background: hsla(210,11%,15%, 0.8);\n", " border: none;\n", "}\n", "#foactivate-76833dd6-a721-43a6-a7e0-0049938076e3:focus {\n", " outline: none;\n", "}\n", "#fooverlay-76833dd6-a721-43a6-a7e0-0049938076e3 {\n", " width: 100%;\n", " height: 100%;\n", " background: hsla(208, 7%, 46%, 0.7);\n", " position: absolute;\n", " top: 0;\n", " left: 0;\n", " display: none;\n", " cursor: pointer;\n", "}\n", "\n", "\n", "
\n", "
\n", " \n", "
\n", " \n", "
\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "session = fo.launch_app(dataset)" ] }, { "cell_type": "code", "execution_count": 6, "id": "4020bab0-e55d-4cd4-9941-271bd92235e5", "metadata": {}, "outputs": [], "source": [ "# This cell is here to close all app and render them as pictures for nbsphinx.\n", "session.freeze()" ] }, { "cell_type": "markdown", "id": "d15202ec-95a3-49f6-bc02-af0debd68ede", "metadata": {}, "source": [ "If you are using a remote notebook, you can either follow [this tutorial](https://docs.voxel51.com/environments/index.html#remote-notebooks) or launch the app with an address set to `0.0.0.0` (which means it accepts all connections)\n", "And then go to your remote machine's ip from another tab\n", "For example, if you remote machine ip is `192.168.40.40`, you will need to visit\n", "the address `192.168.40.40:5151` where `5151` is the configured port (see below)\n", "Uncomment the next cell to test this config" ] }, { "cell_type": "code", "execution_count": 7, "id": "ea2a604f-8e56-4a52-bac0-00391772597d", "metadata": {}, "outputs": [], "source": [ "# fo.launch_app(dataset, auto=False, address=\"0.0.0.0\", port=\"5151\")" ] }, { "cell_type": "markdown", "id": "458b5253", "metadata": {}, "source": [ "## Load a key point dataset in fiftyone\n", "\n", "The key point dataset is similar to bounding box dataset, except it only uses keypoints (with coordinate XY). Fiftyone can deal with it as well.\n", "\n", "Lours will try to convert all key point annotations (i.e. bounding boxes with width and height of 0) to [keypoint fiftyone](https://docs.voxel51.com/api/fiftyone.core.labels.html#fiftyone.core.labels.Keypoints) objects with only one keypoint instead of [Detections](https://docs.voxel51.com/api/fiftyone.core.labels.html#fiftyone.core.labels.Detections) with the option `allow_keypoints`" ] }, { "cell_type": "code", "execution_count": 8, "id": "0fe35e57", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking Image and annotations Ids ...\n", "Checking Bounding boxes ..\n", "Checking label map ...\n", "Checking images are valid ...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6070d8036b82478bbe2ec5fb23ecd562", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\n", "@import url(\"https://fonts.googleapis.com/css2?family=Palanquin&display=swap\");\n", "\n", "body, html {\n", " margin: 0;\n", " padding: 0;\n", " width: 100%;\n", "}\n", "\n", "#focontainer-8ff847d4-504c-4a29-a9d6-54c8cfdcec61 {\n", " position: relative;\n", " height: 800px;\n", " display: block !important;\n", "}\n", "#foactivate-8ff847d4-504c-4a29-a9d6-54c8cfdcec61 {\n", " font-weight: bold;\n", " cursor: pointer;\n", " font-size: 24px;\n", " border-radius: 3px;\n", " text-align: center;\n", " padding: 0.5em;\n", " color: rgb(255, 255, 255);\n", " font-family: \"Palanquin\", sans-serif;\n", " position: absolute;\n", " left: 50%;\n", " top: 50%;\n", " width: 160px;\n", " margin-left: -80px;\n", " margin-top: -23px;\n", " background: hsla(210,11%,15%, 0.8);\n", " border: none;\n", "}\n", "#foactivate-8ff847d4-504c-4a29-a9d6-54c8cfdcec61:focus {\n", " outline: none;\n", "}\n", "#fooverlay-8ff847d4-504c-4a29-a9d6-54c8cfdcec61 {\n", " width: 100%;\n", " height: 100%;\n", " background: hsla(208, 7%, 46%, 0.7);\n", " position: absolute;\n", " top: 0;\n", " left: 0;\n", " display: none;\n", " cursor: pointer;\n", "}\n", "\n", "\n", "
\n", "
\n", " \n", "
\n", " \n", "
\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "session = fo.launch_app(kp_dataset)" ] }, { "cell_type": "code", "execution_count": 11, "id": "b6ad7777-3d0a-491a-a659-fbdc92942ae6", "metadata": {}, "outputs": [], "source": [ "# This cell is here to close all app and render them as pictures for nbsphinx.\n", "session.freeze()" ] }, { "cell_type": "markdown", "id": "1741a4eb", "metadata": {}, "source": [ "## Hybrid dataset\n", "\n", "Lours is also compatible with hybrid datasets, with both detection bounding boxes and keypoints\n", "\n", "Here we create a dataset with some key point bounding boxes and some regular and we show it in fiftyone" ] }, { "cell_type": "code", "execution_count": 12, "id": "9a385364", "metadata": {}, "outputs": [], "source": [ "hybrid_annotations = COCO_dataset.annotations.copy()\n", "i = hybrid_annotations.index\n", "hybrid_annotations.loc[i[:4], \"box_x_min\"] += (\n", " hybrid_annotations.loc[i[:4], \"box_width\"] / 2\n", ")\n", "hybrid_annotations.loc[i[:4], \"box_y_min\"] += (\n", " hybrid_annotations.loc[i[:4], \"box_height\"] / 2\n", ")\n", "hybrid_annotations.loc[i[:4], \"box_width\"] = 0\n", "hybrid_annotations.loc[i[:4], \"box_height\"] = 0\n", "\n", "hybrid_COCO = Dataset(\n", " COCO_dataset.images_root,\n", " COCO_dataset.images,\n", " hybrid_annotations,\n", " COCO_dataset.label_map,\n", ")" ] }, { "cell_type": "code", "execution_count": 13, "id": "d62ae1cd", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8a8274876e76422a9033aa7e199d02d0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hybrid_COCO" ] }, { "cell_type": "code", "execution_count": 14, "id": "f46abeb4", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "812622a6a6ea4252aa8eabf519201411", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\n", "@import url(\"https://fonts.googleapis.com/css2?family=Palanquin&display=swap\");\n", "\n", "body, html {\n", " margin: 0;\n", " padding: 0;\n", " width: 100%;\n", "}\n", "\n", "#focontainer-5faaf9bc-ded5-4c9e-867c-8df0a95233d4 {\n", " position: relative;\n", " height: 800px;\n", " display: block !important;\n", "}\n", "#foactivate-5faaf9bc-ded5-4c9e-867c-8df0a95233d4 {\n", " font-weight: bold;\n", " cursor: pointer;\n", " font-size: 24px;\n", " border-radius: 3px;\n", " text-align: center;\n", " padding: 0.5em;\n", " color: rgb(255, 255, 255);\n", " font-family: \"Palanquin\", sans-serif;\n", " position: absolute;\n", " left: 50%;\n", " top: 50%;\n", " width: 160px;\n", " margin-left: -80px;\n", " margin-top: -23px;\n", " background: hsla(210,11%,15%, 0.8);\n", " border: none;\n", "}\n", "#foactivate-5faaf9bc-ded5-4c9e-867c-8df0a95233d4:focus {\n", " outline: none;\n", "}\n", "#fooverlay-5faaf9bc-ded5-4c9e-867c-8df0a95233d4 {\n", " width: 100%;\n", " height: 100%;\n", " background: hsla(208, 7%, 46%, 0.7);\n", " position: absolute;\n", " top: 0;\n", " left: 0;\n", " display: none;\n", " cursor: pointer;\n", "}\n", "\n", "\n", "

\n", "
\n", " \n", "
\n", " \n", "
\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "session = fo.launch_app(hybrid_dataset)" ] }, { "cell_type": "code", "execution_count": 16, "id": "e4387ead-cba5-4163-8465-2eb21b21e353", "metadata": {}, "outputs": [], "source": [ "# This cell is here to close all app and render them as pictures for nbsphinx.\n", "session.freeze()" ] }, { "cell_type": "markdown", "id": "6fe18344-d4fe-4d8a-b80f-d7df4d5ff473", "metadata": {}, "source": [ "## Load multiple annotations sets to the same fiftyone dataset\n", "\n", "You can easily compare two sets of annotations with fiftyone. This is very useful for inspecting predictions with respect to the ground truth or other predictions\n", "\n", "In this example, we construct a fictive dataset with fair predictions based on COCO_dataset." ] }, { "cell_type": "code", "execution_count": 17, "id": "d34f5b3a-ea9d-4a89-85cc-6deffeacf1bf", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "COCO_dataset_predictions = COCO_dataset.empty_annotations()\n", "with COCO_dataset_predictions.annotation_append() as appender:\n", " appender.append(\n", " image_id=9,\n", " category_id=51,\n", " bbox_coordinates=np.array(\n", " [[0, 200, 600, 300], [300, 0, 300, 250], [0, 0, 400, 400]]\n", " ),\n", " )\n", " appender.append(\n", " image_id=34, category_id=24, bbox_coordinates=np.array([[0, 20, 450, 400]])\n", " )" ] }, { "cell_type": "code", "execution_count": 18, "id": "881e4831-5424-47ce-bc60-85fd66b2ad4f", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f0914b146e8748538daa923cdf6b9121", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=\"

Dataset object containing…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "COCO_dataset_predictions" ] }, { "cell_type": "markdown", "id": "a551c5b7-93d3-4431-b616-4d2dfcf96b8d", "metadata": {}, "source": [ "### Using multiple Lours dataset objects\n", "\n", "The first option to compare two datasets in fiftyone is to call the `to_fiftyone` method with the same dataset name as the first dataset, but with a different `annotations_name`" ] }, { "cell_type": "code", "execution_count": 19, "id": "0b539301-2975-4bf2-83d2-4233af35fd79", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "266ce8e64c5b4890a65d27833b1bb77e", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\n", "@import url(\"https://fonts.googleapis.com/css2?family=Palanquin&display=swap\");\n", "\n", "body, html {\n", " margin: 0;\n", " padding: 0;\n", " width: 100%;\n", "}\n", "\n", "#focontainer-2a452ffb-c8af-4ba9-88f0-6feb4bc3b015 {\n", " position: relative;\n", " height: 800px;\n", " display: block !important;\n", "}\n", "#foactivate-2a452ffb-c8af-4ba9-88f0-6feb4bc3b015 {\n", " font-weight: bold;\n", " cursor: pointer;\n", " font-size: 24px;\n", " border-radius: 3px;\n", " text-align: center;\n", " padding: 0.5em;\n", " color: rgb(255, 255, 255);\n", " font-family: \"Palanquin\", sans-serif;\n", " position: absolute;\n", " left: 50%;\n", " top: 50%;\n", " width: 160px;\n", " margin-left: -80px;\n", " margin-top: -23px;\n", " background: hsla(210,11%,15%, 0.8);\n", " border: none;\n", "}\n", "#foactivate-2a452ffb-c8af-4ba9-88f0-6feb4bc3b015:focus {\n", " outline: none;\n", "}\n", "#fooverlay-2a452ffb-c8af-4ba9-88f0-6feb4bc3b015 {\n", " width: 100%;\n", " height: 100%;\n", " background: hsla(208, 7%, 46%, 0.7);\n", " position: absolute;\n", " top: 0;\n", " left: 0;\n", " display: none;\n", " cursor: pointer;\n", "}\n", "\n", "\n", "

\n", "
\n", " \n", "
\n", " \n", "
\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "session = fo.launch_app(dataset)" ] }, { "cell_type": "code", "execution_count": 21, "id": "3b937c32-3620-4b57-ad63-0eaf14226b2b", "metadata": {}, "outputs": [], "source": [ "# This cell is here to close all app and render them as pictures for nbsphinx.\n", "session.freeze()" ] }, { "cell_type": "markdown", "id": "79d568da-2058-4da3-b1fe-6b0223426519", "metadata": {}, "source": [ "### Using Evaluator object\n", "\n", "The second option is to let Lours handle both dataset at the same time with a `Evaluator` object. See [related python](3_demo_evaluation_detection.ipynb) notebook for Evaluation. Note that the evaluator method only works if the predictions dataset has a `confidence` column." ] }, { "cell_type": "code", "execution_count": 22, "id": "8edf2f1a-063a-4310-865c-42d58605006c", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a59a7a250e784ef794527b0e6ecce8b8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=' Evaluation object, containing 2 images, 9 groundtruth objects, and 1 prediction…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lours.evaluation import DetectionEvaluator as de\n", "\n", "COCO_dataset_predictions.annotations[\"confidence\"] = 1\n", "evaluator = de(groundtruth=COCO_dataset, predictions=COCO_dataset_predictions)\n", "\n", "display(evaluator)" ] }, { "cell_type": "code", "execution_count": 23, "id": "9cd2304d-ad2a-4961-8174-bf0f57292e06", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aca94fd527e5455f940fb46ff0fc13aa", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/2 [00:00\n", "@import url(\"https://fonts.googleapis.com/css2?family=Palanquin&display=swap\");\n", "\n", "body, html {\n", " margin: 0;\n", " padding: 0;\n", " width: 100%;\n", "}\n", "\n", "#focontainer-61b7a0a6-69ba-4596-bcea-1f80f1a705e6 {\n", " position: relative;\n", " height: 800px;\n", " display: block !important;\n", "}\n", "#foactivate-61b7a0a6-69ba-4596-bcea-1f80f1a705e6 {\n", " font-weight: bold;\n", " cursor: pointer;\n", " font-size: 24px;\n", " border-radius: 3px;\n", " text-align: center;\n", " padding: 0.5em;\n", " color: rgb(255, 255, 255);\n", " font-family: \"Palanquin\", sans-serif;\n", " position: absolute;\n", " left: 50%;\n", " top: 50%;\n", " width: 160px;\n", " margin-left: -80px;\n", " margin-top: -23px;\n", " background: hsla(210,11%,15%, 0.8);\n", " border: none;\n", "}\n", "#foactivate-61b7a0a6-69ba-4596-bcea-1f80f1a705e6:focus {\n", " outline: none;\n", "}\n", "#fooverlay-61b7a0a6-69ba-4596-bcea-1f80f1a705e6 {\n", " width: 100%;\n", " height: 100%;\n", " background: hsla(208, 7%, 46%, 0.7);\n", " position: absolute;\n", " top: 0;\n", " left: 0;\n", " display: none;\n", " cursor: pointer;\n", "}\n", "\n", "\n", "
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\n", "\n", "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "session = fo.launch_app(comparator)" ] }, { "cell_type": "code", "execution_count": 25, "id": "43dcf650-575b-45e5-8855-edf49fa5e626", "metadata": {}, "outputs": [], "source": [ "# This cell is here to close all app and render them as pictures for nbsphinx.\n", "session.freeze()\n", "fo.close_app()" ] } ], "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": { "033af590e1c145d99193ae47e6935f42": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_986ff310258e4fd5b5dc6ce80ae311f0", "IPY_MODEL_1cf3e2226a094abfa1a7aefd5331fc55" ], "layout": "IPY_MODEL_04a24b70f88145c2837f44a20b29f065" } }, "0422d08f0f214068b0cf45f6259bdb8b": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "0452a57e329e4589b1ee24f66d909d36": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "048d01a9c32d42efaaf93955269eb353": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_0422d08f0f214068b0cf45f6259bdb8b", "outputs": [ { "data": { "text/html": "
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34640425train/000000000034.png.png
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category string
categorty_id
1person
23bear
24zebra
51bowl
55orange
56broccoli
62chair
64potted plant
67dining table
72tv
78microwave
82refrigerator
84book
85clock
86vase
\n
", "text/plain": " category string\ncategorty_id \n1 person\n23 bear\n24 zebra\n51 bowl\n55 orange\n56 broccoli\n62 chair\n64 potted plant\n67 dining table\n72 tv\n78 microwave\n82 refrigerator\n84 book\n85 clock\n86 vase" }, "metadata": {}, "output_type": "display_data" } ] } }, "15f92f684c8d417cac49c846de8a5b66": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "173bc58de70746708388881399fc9de1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_a7da401cb15d4c759a17a2d63385f436", "max": 2, "style": "IPY_MODEL_c83d580c646641d7a94b5a8b4416eff7", "value": 2 } }, "19780817369c42f8b568a9653b8523d7": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_f55f1e84926f4f579da02f208dca38a6", "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
category string
category_id
1person
23bear
24zebra
51bowl
55orange
56broccoli
62chair
64potted plant
67dining table
72tv
78microwave
82refrigerator
84book
85clock
86vase
\n
", "text/plain": " category string\ncategory_id \n1 person\n23 bear\n24 zebra\n51 bowl\n55 orange\n56 broccoli\n62 chair\n64 potted plant\n67 dining table\n72 tv\n78 microwave\n82 refrigerator\n84 book\n85 clock\n86 vase" }, "metadata": {}, "output_type": "display_data" } ] } }, "1a0d44386b8c40399a0071a3059b4de3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1be4500d35fd4a3ca3e621c73337b293": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "1cf3e2226a094abfa1a7aefd5331fc55": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_d12ac82720c34af6b8bc0aa437832acd", "IPY_MODEL_420084448d3c49b78bad9521fb0a8ff5", "IPY_MODEL_19780817369c42f8b568a9653b8523d7" ], "layout": "IPY_MODEL_4777c97026274d8bad2170aa6aa40af5", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "1e95f4a090f4447292ab8ad0094cf217": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_95032dbb02674df4b96423625cbd7742", "style": "IPY_MODEL_04f556a381454887ae38c8ecd477d2b6", "value": " 2/2 [00:00<00:00, 400.64it/s]" } }, "1fe880998c704b92b788f224afbcdfb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "23f542b07f8442b7b581b79e3e60d8b8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "266ce8e64c5b4890a65d27833b1bb77e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_711495087e2f4c67a814fdf145944e97", "IPY_MODEL_4e96997d9a8240e0a34e44258dd30559", "IPY_MODEL_b9cc856424dc4186b5081a313f383eeb" ], "layout": "IPY_MODEL_c601d46051db4dd29969b2d54d95605f" } }, "27ecf58092894bee9955aa7341f2e137": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_73852560a48d44ae864607dc0caa2c34", "max": 2, "style": "IPY_MODEL_b6b9b0053d6041a29361df329526e22c", "value": 2 } }, "2ad2a4182bee4bd5b6b0e2c63c7378c3": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "318894cc166c4ca3a815bb639a5cb7d3": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_fd5c827497064641905f3b12b2e7f299", "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
9640480train/000000000009.png.pngtrain
34640425train/000000000034.png.pngtrain
\n
", "text/plain": " width height relative_path type split\nid \n9 640 480 train/000000000009.png .png train\n34 640 425 train/000000000034.png .png train" }, "metadata": {}, "output_type": "display_data" } ] } }, "32113825765b4e6b8a482ae831b348c6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "33fb755221124d0e897b9fc6e5593615": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "388345b35ee94b48876c7203a2ee538a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3c4512deebf04722b7195e02d7b1c2a6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "3c92248d312d45fbac6f3f83036bf2b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "420084448d3c49b78bad9521fb0a8ff5": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_124425c78fe34577b0c9acb08c7eea91", "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
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
10389679bowl51train1.08187.69611.59285.84120057.13925
10395649bowl51train311.734.31319.28228.6844434.75110
10585559broccoli56train249.60229.27316.24245.0849577.94435
15341479bowl51train0.0013.51434.48375.1224292.78170
19135519orange55train376.2040.3675.5546.532239.29240
19137469orange55train465.7838.9758.0746.671658.89130
19138569orange55train385.7073.6684.0270.513609.30305
19140019orange55train364.052.4994.7671.072975.27600
58922934zebra24train0.9620.06441.23379.1592920.15370
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n1038967 9 bowl 51 train 1.08 187.69 \n1039564 9 bowl 51 train 311.73 4.31 \n1058555 9 broccoli 56 train 249.60 229.27 \n1534147 9 bowl 51 train 0.00 13.51 \n1913551 9 orange 55 train 376.20 40.36 \n1913746 9 orange 55 train 465.78 38.97 \n1913856 9 orange 55 train 385.70 73.66 \n1914001 9 orange 55 train 364.05 2.49 \n589229 34 zebra 24 train 0.96 20.06 \n\n box_width box_height area \nid \n1038967 611.59 285.84 120057.13925 \n1039564 319.28 228.68 44434.75110 \n1058555 316.24 245.08 49577.94435 \n1534147 434.48 375.12 24292.78170 \n1913551 75.55 46.53 2239.29240 \n1913746 58.07 46.67 1658.89130 \n1913856 84.02 70.51 3609.30305 \n1914001 94.76 71.07 2975.27600 \n589229 441.23 379.15 92920.15370 " }, "metadata": {}, "output_type": "display_data" } ] } }, "43e3b7afe6d740bb9bab098866e06a6f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "466b9a2e914548bba594f56e757a7de7": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4777c97026274d8bad2170aa6aa40af5": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "4e96997d9a8240e0a34e44258dd30559": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_3c4512deebf04722b7195e02d7b1c2a6", "max": 2, "style": "IPY_MODEL_fc5621aeada84528835dc1f9f4b603dd", "value": 2 } }, "51eee840827c4a5ab778208662a02924": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_e1041b8d294e4d498da75e764e195743", "IPY_MODEL_bb7cbedb0226499089a4c1cf7256686f", "IPY_MODEL_6524f1c7a8494d83a4ee091428f56ef4" ], "layout": "IPY_MODEL_32113825765b4e6b8a482ae831b348c6", "selected_index": 0, "titles": [ "Images", "Annotations", "Label Map" ] } }, "57b34a928d654c148a738bd0bb69e9f6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "59f11883a84e4681aeb75284d8a4f3ef": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_05ff8c6335f94ecd8138b7346c946939", "max": 2, "style": "IPY_MODEL_07f51a4ac04647009400a418b9aeb4b1", "value": 2 } }, "5a4a2f8778e542149ada170048d68f48": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "5b8c08f7f42840a7af5b7876e28f32d8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6070d8036b82478bbe2ec5fb23ecd562": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_b306db780ac94e8d9651646f4ede1825", "IPY_MODEL_173bc58de70746708388881399fc9de1", "IPY_MODEL_b41ace6e22384526af27972bf98a15b4" ], "layout": "IPY_MODEL_60ae4b4d7bb64954bb79b42ccf1f6cde" } }, "60ae4b4d7bb64954bb79b42ccf1f6cde": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6254caf36c52480dafe0372edc7efabc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_6dfec242dcea461a84c2f87a377a7495", "style": "IPY_MODEL_6e0bbe1d9a144a929485d344652f94a3", "value": "

Dataset object containing 2 images and 4 objects\nName :\n\tannotations\nImages root :\n\t../../test_lours/test_data/coco_dataset/data/Images

" } }, "641dffc6169d428781cfa441c5eb2b16": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_2ad2a4182bee4bd5b6b0e2c63c7378c3", "style": "IPY_MODEL_82b42569b0cf40ea86437d5924b7b197", "value": " 2/2 [00:00<00:00, 425.90it/s]" } }, "6524f1c7a8494d83a4ee091428f56ef4": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_7ff6b793af3c4662b3dd958e21af6b22", "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
category string
category_id
1person
23bear
24zebra
51bowl
55orange
56broccoli
62chair
64potted plant
67dining table
72tv
78microwave
82refrigerator
84book
85clock
86vase
\n
", "text/plain": " category string\ncategory_id \n1 person\n23 bear\n24 zebra\n51 bowl\n55 orange\n56 broccoli\n62 chair\n64 potted plant\n67 dining table\n72 tv\n78 microwave\n82 refrigerator\n84 book\n85 clock\n86 vase" }, "metadata": {}, "output_type": "display_data" } ] } }, "6b2a9ab36fcf436cbad48d12a9ca9394": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_bceef12ef99f42159be90a8fddcd20ee", "max": 2, "style": "IPY_MODEL_43e3b7afe6d740bb9bab098866e06a6f", "value": 2 } }, "6d9570de1b90409092b1069624ad0dd8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "6dfec242dcea461a84c2f87a377a7495": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "6e0bbe1d9a144a929485d344652f94a3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "711495087e2f4c67a814fdf145944e97": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_3c92248d312d45fbac6f3f83036bf2b0", "style": "IPY_MODEL_5a4a2f8778e542149ada170048d68f48", "value": "100%" } }, "7346f47efbd7455aac5c8851f17d7144": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_0452a57e329e4589b1ee24f66d909d36", "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
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
10389679bowl51train1.08187.69611.59285.84120057.13925
10395649bowl51train311.734.31319.28228.6844434.75110
10585559broccoli56train249.60229.27316.24245.0849577.94435
15341479bowl51train0.0013.51434.48375.1224292.78170
19135519orange55train376.2040.3675.5546.532239.29240
19137469orange55train465.7838.9758.0746.671658.89130
19138569orange55train385.7073.6684.0270.513609.30305
19140019orange55train364.052.4994.7671.072975.27600
58922934zebra24train0.9620.06441.23379.1592920.15370
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n1038967 9 bowl 51 train 1.08 187.69 \n1039564 9 bowl 51 train 311.73 4.31 \n1058555 9 broccoli 56 train 249.60 229.27 \n1534147 9 bowl 51 train 0.00 13.51 \n1913551 9 orange 55 train 376.20 40.36 \n1913746 9 orange 55 train 465.78 38.97 \n1913856 9 orange 55 train 385.70 73.66 \n1914001 9 orange 55 train 364.05 2.49 \n589229 34 zebra 24 train 0.96 20.06 \n\n box_width box_height area \nid \n1038967 611.59 285.84 120057.13925 \n1039564 319.28 228.68 44434.75110 \n1058555 316.24 245.08 49577.94435 \n1534147 434.48 375.12 24292.78170 \n1913551 75.55 46.53 2239.29240 \n1913746 58.07 46.67 1658.89130 \n1913856 84.02 70.51 3609.30305 \n1914001 94.76 71.07 2975.27600 \n589229 441.23 379.15 92920.15370 " }, "metadata": {}, "output_type": "display_data" } ] } }, "73852560a48d44ae864607dc0caa2c34": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "741cfc0119344c538ced4acd788c40fb": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_23f542b07f8442b7b581b79e3e60d8b8", "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
category string
category_id
1person
23bear
24zebra
51bowl
55orange
56broccoli
62chair
64potted plant
67dining table
72tv
78microwave
82refrigerator
84book
85clock
86vase
\n
", "text/plain": " category string\ncategory_id \n1 person\n23 bear\n24 zebra\n51 bowl\n55 orange\n56 broccoli\n62 chair\n64 potted plant\n67 dining table\n72 tv\n78 microwave\n82 refrigerator\n84 book\n85 clock\n86 vase" }, "metadata": {}, "output_type": "display_data" } ] } }, "7597072459fe4dd2a6b8702fdf9439c5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "7698271f416b4f14a6d95a45d985025e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "7aabeb9d1114477385ed7ce69d39d72a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_0e5a1d05b7f9491bade87f2fc3115414", "IPY_MODEL_be39ba9ebb024ac2a4cb2bebb3bfe2d3", "IPY_MODEL_e2401d4ab8a942d0b8c4748d6e68ff76" ], "layout": "IPY_MODEL_edec845fb16c4db793330eafc461a7ff" } }, "7ff6b793af3c4662b3dd958e21af6b22": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "8079eb92d3ca4672a050e8db22fdb94e": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "812622a6a6ea4252aa8eabf519201411": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "children": [ "IPY_MODEL_e6f9952978804463972045a89fce93d0", "IPY_MODEL_59f11883a84e4681aeb75284d8a4f3ef", "IPY_MODEL_c078e0837af34db9859e48770c38134a" ], "layout": "IPY_MODEL_e366c6329ff24c19a4fe311d837d3029" } }, "819d6090478c4c00a926cd968d35065a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "82b42569b0cf40ea86437d5924b7b197": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "84464210f87d4a6b936cb8951f926da9": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "848543d87f3d4bb481248eba74d8e5ed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_e54c0a64ef3c41c0bae47d30506c7ad0", "style": "IPY_MODEL_e8ba987d1cbb4352bc9b7de20ce6de52", "value": "100%" } }, "8a8274876e76422a9033aa7e199d02d0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "VBoxModel", "state": { "children": [ "IPY_MODEL_9e238594ad304fdca7a1f999c322d08d", "IPY_MODEL_f470f451c53a40d289869065af6a683a" ], "layout": "IPY_MODEL_f5420779f2e74b73b2f9e01f15499899" } }, "8d680a3bf3ae4e0bb3ed3a1d5783ec44": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_07b815fc14a04618a583116dae23fc58", "style": "IPY_MODEL_cc7ca88e09ca4203a6e9cb34d60f8872", "value": "100%" } }, "8e8eb451e60a40c68fa90c79791edaeb": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9162ead3d3944e9eb2895a3a6fd20b61": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "95032dbb02674df4b96423625cbd7742": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "95f5388ac9744fd081c449b512311638": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "96ac985be1724693a6c2dd92367df35b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_8e8eb451e60a40c68fa90c79791edaeb", "style": "IPY_MODEL_a87d418e3f814a328b524d902ad687c9", "value": " 2/2 [00:00<00:00, 34.01it/s]" } }, "9836fa5ca9af4b23bdf78b1432fdca50": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "986ff310258e4fd5b5dc6ce80ae311f0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_f20216c17c4742f396b7c832a63afd90", "style": "IPY_MODEL_d33e812508c74b61a19be4a4012904af", "value": "

Dataset object containing 2 images and 9 objects\nName :\n\tannotations\nImages root :\n\t../../test_lours/test_data/coco_dataset/data/Images

" } }, "9b1f81fc2f3e4d96a11e69938f743657": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_b6163b176ed846718d3b897c425bd249", "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
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightareaconfidence
id
09bowl51train0.0200.0600.0300.0180000.01
19bowl51train300.00.0300.0250.075000.01
29bowl51train0.00.0400.0400.0160000.01
334zebra24train0.020.0450.0400.0180000.01
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 9 bowl 51 train 0.0 200.0 \n1 9 bowl 51 train 300.0 0.0 \n2 9 bowl 51 train 0.0 0.0 \n3 34 zebra 24 train 0.0 20.0 \n\n box_width box_height area confidence \nid \n0 600.0 300.0 180000.0 1 \n1 300.0 250.0 75000.0 1 \n2 400.0 400.0 160000.0 1 \n3 450.0 400.0 180000.0 1 " }, "metadata": {}, "output_type": "display_data" } ] } }, "9c29bef652ee4e328c00df5d60a18851": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "9e238594ad304fdca7a1f999c322d08d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_388345b35ee94b48876c7203a2ee538a", "style": "IPY_MODEL_6d9570de1b90409092b1069624ad0dd8", "value": "

Dataset object containing 2 images and 9 objects\nName :\n\tNone\nImages root :\n\t../../test_lours/test_data/coco_dataset/data/Images

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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 \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
09bowl51train0.0200.0600.0300.0180000.0
19bowl51train300.00.0300.0250.075000.0
29bowl51train0.00.0400.0400.0160000.0
334zebra24train0.020.0450.0400.0180000.0
\n
", "text/plain": " image_id category_str category_id split box_x_min box_y_min \\\nid \n0 9 bowl 51 train 0.0 200.0 \n1 9 bowl 51 train 300.0 0.0 \n2 9 bowl 51 train 0.0 0.0 \n3 34 zebra 24 train 0.0 20.0 \n\n box_width box_height area \nid \n0 600.0 300.0 180000.0 \n1 300.0 250.0 75000.0 \n2 400.0 400.0 160000.0 \n3 450.0 400.0 180000.0 " }, "metadata": {}, "output_type": "display_data" } ] } }, "bceef12ef99f42159be90a8fddcd20ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "be39ba9ebb024ac2a4cb2bebb3bfe2d3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "bar_style": "success", "layout": "IPY_MODEL_e9cbadee066e42368a8b8de67c1529c3", "max": 2, "style": "IPY_MODEL_dfbdcd4c56d64e9d84f55131a08419c3", "value": 2 } }, "c078e0837af34db9859e48770c38134a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "layout": "IPY_MODEL_092c645a7d504d2bb925c1d4d991a139", "style": "IPY_MODEL_eb7a7c7d49534f7e96d4f78c5f1133f2", "value": " 2/2 [00:00<00:00, 415.55it/s]" } }, "c21ab4b778244e4cb2ad57dd713f8274": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "TabModel", "state": { "children": [ "IPY_MODEL_048d01a9c32d42efaaf93955269eb353", "IPY_MODEL_7346f47efbd7455aac5c8851f17d7144", "IPY_MODEL_9b1f81fc2f3e4d96a11e69938f743657", "IPY_MODEL_1346bc9ac6ea4d069f75954b8b9d3db0" ], "layout": "IPY_MODEL_ebb1106dfba044d0a1600060af6718a7", "selected_index": 0, "titles": [ "Images", "Groundtruth", "predictions", "label_map" ] } }, "c601d46051db4dd29969b2d54d95605f": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "c83d580c646641d7a94b5a8b4416eff7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": { "description_width": "" } }, "cc00d2dcc7724b579385532cf72639ce": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "cc7ca88e09ca4203a6e9cb34d60f8872": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "d12ac82720c34af6b8bc0aa437832acd": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_84464210f87d4a6b936cb8951f926da9", "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
9640480train/000000000009.png.pngtrain
34640425train/000000000034.png.pngtrain
\n
", "text/plain": " width height relative_path type split\nid \n9 640 480 train/000000000009.png .png train\n34 640 425 train/000000000034.png .png train" }, "metadata": {}, "output_type": "display_data" } ] } }, "d33e812508c74b61a19be4a4012904af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "description_width": "", "font_size": null, "text_color": null } }, "d8077ee806734844b804a0057385f1aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "da5032b14f7243f88566e0b02ea64cc8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": {} }, "de205e4b48c04c7187e7a445b172b086": { "model_module": "@jupyter-widgets/output", "model_module_version": "1.0.0", "model_name": "OutputModel", "state": { "layout": "IPY_MODEL_15f92f684c8d417cac49c846de8a5b66", "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
image_idcategory_strcategory_idsplitbox_x_minbox_y_minbox_widthbox_heightarea
id
10389679bowl51train306.875330.610.000.00120057.13925
10395649bowl51train471.370118.650.000.0044434.75110
10585559broccoli56train407.720351.810.000.0049577.94435
15341479bowl51train217.240201.070.000.0024292.78170
19135519orange55train376.20040.3675.5546.532239.29240
19137469orange55train465.78038.9758.0746.671658.89130
19138569orange55train385.70073.6684.0270.513609.30305
19140019orange55train364.0502.4994.7671.072975.27600
58922934zebra24train0.96020.06441.23379.1592920.15370
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