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Merge pull request #225 from Labelbox/ms/3.0-notebook-updates
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‎CHANGELOG.md

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# Changelog
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# Version 3.0.0-rc0
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# Version 3.0.0-rc1 (2021-08-05)
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## Added
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* Model diagnostics notebooks
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* Minor annotation type improvements
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# Version 3.0.0-rc0 (2021-08-04)
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## Added
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* Annotation types
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- A set of python objects for working with labelbox data

‎examples/README.md

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| Annotation Type Basics | [Github](annotation_types/basics.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/basics.ipynb) |
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| Converters | [Github](annotation_types/converters.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/converters.ipynb) |
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| Label Containers | [Github](annotation_types/label_containers.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/label_containers.ipynb) |
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| MAL With Annotation Types | [Github](annotation_types/mal_with_annotation_types.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/annotation_types/mal_with_annotation_types.ipynb) |
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## [Model Diagnostics](model_diagnostics)
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| Notebook | Github | Google Colab |
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| --------------------------- | --------------------------------- | ------------ |
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| Model Diagnostics Guide | [Github](model_diagnostics/model_diagnostics_guide.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_diagnostics/model_diagnostics_guide.ipynb) |
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| Model Diagnostics Demo | [Github](model_diagnostics/model_diagnostics_demo.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Labelbox/labelbox-python/blob/develop/examples/model_diagnostics/model_diagnostics_demo.ipynb) |

‎examples/annotation_types/basics.ipynb

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‎examples/annotation_types/converters.ipynb

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‎examples/annotation_types/label_containers.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install labelbox[data] == 3.0b0"
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"!pip install \"labelbox[data]==3.0.0rc1\""
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]
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},
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{
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"source": [
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"# Only update this if you have an on-prem deployment\n",
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"ENDPOINT = \"https://api.labelbox.com/graphql\"\n",
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"\n",
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"client = Client(api_key=API_KEY, endpoint=ENDPOINT)"
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]
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},
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"metadata": {},
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"source": [
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"#### Helper Functions\n",
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"* See annotation_type_basics for details on this"
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"* See basics for details on this"
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]
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},
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{
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"outputs": [],
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"source": [
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"labels = get_labels()\n",
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"label_list = LabelList(labels)"
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"label_list = LabelList(labels)\n",
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"\n",
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"# Also build label lists iteratively\n",
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"label_list = LabelList()\n",
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"for label in labels:\n",
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" label_list.append(label)"
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]
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},
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{
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},

‎examples/annotation_types/mal_with_annotation_types.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"id": "danish-compatibility",
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip labelbox[data] == 3.0b0"
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"!pip install \"labelbox[data]==3.0.0rc1\""
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]
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},
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{
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"cell_type": "code",
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"id": "honey-elevation",
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},
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"cell_type": "code",
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},
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"id": "hydraulic-paradise",
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},
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},
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"execution_count": null,
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"id": "polyphonic-consensus",
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},
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"cell_type": "code",
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"execution_count": null,
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"id": "emotional-origin",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"WARNING:tensorflow:From ../model_assisted_labeling/image_model.py:17: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.\n",
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"INFO:tensorflow:Restoring parameters from gs://cloud-tpu-checkpoints/mask-rcnn/1555659850/variables/variables\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"#Downloads weights and loads the model.\n",
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"load_model()"
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": null,
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"id": "interested-joyce",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "interstate-louisiana",
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"metadata": {},
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"outputs": [],
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"source": [
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"### We can just start creating predictions whether or not we have a \n",
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"# We can just start creating predictions whether or not we have a configured project in labelbox\n",
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"image_urls = ['https://raw.githubusercontent.com/Labelbox/labelbox-python/develop/examples/assets/2560px-Kitano_Street_Kobe01s5s4110.jpg']\n",
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"\n",
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"labellist = LabelList([])\n",
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"\n",
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"labellist = LabelList()\n",
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"for image_url in image_urls:\n",
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" image_data = RasterData(url = image_url)\n",
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" height, width = image_data.data.shape[:2]\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "modern-program",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# Lets setup a project to label\n",
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"# Note see Ontology, Project, and Project_setup notebooks for more information on this section.\n",
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"id": "mechanical-functionality",
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"metadata": {},
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"source": [
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"## Add ids required for MAL\n",
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"* Use helper functions to add urls to images and seg masks, assign schema ids to features, and add all data rows to the dataset."
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"## Prepare for upload\n",
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"* Our local annotations need the following:\n",
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" 1. signed url for segmentation masks\n",
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" 2. data rows in labelbox\n",
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" 3. feature schema ids"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "vietnamese-kingdom",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"text": [
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"1it [00:02, 2.06s/it]\n",
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"1it [00:00, 11366.68it/s]\n",
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"1it [00:00, 9020.01it/s]\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<labelbox.data.annotation_types.collection.LabelList at 0x18855b040>"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"signer = lambda _bytes: client.upload_data(content=_bytes, sign=True)\n",
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"labellist.add_url_to_masks(signer) \\\n",
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},
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{
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"cell_type": "code",
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"metadata": {},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'uuid': '2f708795-c8bf-4793-88d6-18ab57193466', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'schemaId': 'ckros4zfh2jjq0z8b6jq8h8u5', 'classifications': [{'schemaId': 'ckros4zg72jjw0z8ba10r0ozr', 'answer': {'schemaId': 'ckros4zgx2jjy0z8bbyuv6z1o'}}], 'bbox': {'top': 1352.3682861328125, 'left': 2275.82861328125, 'height': 350.1317138671875, 'width': 139.7919921875}}\n"
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]
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],
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"outputs": [],
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"print(ndjsons[0])"
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{
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"{'uuid': '2f708795-c8bf-4793-88d6-18ab57193466', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '383e8da0-ae5d-47d8-8a13-767a03270d40', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'e66676df-eba3-4231-82c7-5870f4e587be', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'ba1e748b-3bcc-41f1-b5d6-f7c420c95b14', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '4ce19f3a-7d81-420c-a830-47eda0034fc7', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '3491fa2f-39cb-4582-b2e4-c7196a3bb37e', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '97d17219-9f3e-4c6e-af7a-0c263445405b', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'b829c83b-8741-4ea4-9f98-0478fdde2bf0', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '68889fc5-2f10-4929-8c7b-8e4301cf17bf', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'b7a6025d-fd8a-46e8-a24d-2340be021d69', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'e8a46ebc-30ac-41bd-b0b9-042b99774dc3', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'ba1d40b9-eb6d-4eae-babc-08e14b76eedc', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'd81bea0a-baf1-4a70-902c-e2f251cb82d2', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '9bf9337f-1550-4dc8-b969-f9c294a44355', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '0dd0d47e-f512-46c2-a670-62044085b01b', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '7196cc1d-b8e5-4ad3-bc28-d33c83dbd8c2', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '51e67fba-d5f2-44b1-835a-71d66365411c', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '90d8e3e9-5184-4500-8b9c-dc690e20e6e9', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'ed2f9581-5dc7-4748-ab1e-28c85b601ba5', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '7ba3b5b6-3ec3-4fe5-ba7a-bc6edf017af7', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '72555669-9899-4e5f-a683-f7676aa9ac4a', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'b0cbc556-ce6b-459f-9420-f1146c97a944', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '1f17e1c1-9b48-4638-9bdc-3f62a1cba83e', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '4772a3df-8072-4e33-a6c9-36e94470a8b2', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '892aa14a-7b37-4f23-a014-20f37b45c17a', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'bc6a665f-95c2-450c-a2bb-d9578e8f5b5a', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': 'eb2c61a4-fce8-400e-b981-19f03b5a38ed', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '3f22c739-33ff-4673-a7bb-953aed76e2b2', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '12eaaf77-d7e3-4375-bdc8-2e1588c718d5', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '0713fea3-d2e5-4ed4-b722-a6a65c19afc0', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '4a629d43-f576-4a7e-b64e-870d3309a98d', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"{'uuid': '5010efef-e45f-4cbe-a92e-86a62962d6e9', 'dataRow': {'id': 'ckros561421gc0zu0eoae5ume'}, 'status': 'SUCCESS'}\n",
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"outputs": [],
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"metadata": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},

‎examples/model_assisted_labeling/mapillary_sample.csv renamed to ‎examples/assets/mapillary_sample.csv

File renamed without changes.

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