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CHANGELOG.md

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# Changelog
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# Version 3.25.0 (2022-07-20)
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## Added
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* Importing annotations with model assisted labeling or label imports using ontology object names instead of schemaId now possible
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* In Python dictionaries, you can now use `schemaId` key or `name` key for all tools, classifications, options
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* Labelbox's Annotation Types now support model assisted labeling or label imports using ontology object names
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* Export metadata when using the following methods:
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* `Batch.export_data_rows(include_metadata=True)`
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* `Dataset.export_data_rows(include_metadata=True)`
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* `Project.export_queued_data_rows(include_metadata=True)`
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* `VideoObjectAnnotation` has `segment_index` to group video annotations into video segments
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## Removed
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* `Project.video_label_generator`. Use `Project.label_generator` instead.
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## Updated
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* Model Runs now support unassigned splits
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* `Dataset.create_data_rows` now has the following limits:
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* 150,000 rows per upload without metadata
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* 30,000 rows per upload with metadata
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# Version 3.24.1 (2022-07-07)
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## Updated
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* Added `refresh_ontology()` as part of create/update/delete metadata schema functions

docs/source/conf.py

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copyright = '2021, Labelbox'
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author = 'Labelbox'
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release = '3.11.0'
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release = '3.25.1'
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# -- General configuration ---------------------------------------------------
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docs/source/index.rst

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.. automodule:: labelbox.schema.annotation_import
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:members:
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:show-inheritance:
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Batch
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----------------------------
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.. automodule:: labelbox.schema.batch
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:members:
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:show-inheritance:
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ResourceTag
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----------------------------
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.. automodule:: labelbox.schema.resource_tag
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:members:
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:show-inheritance:

examples/annotation_import/basics.ipynb

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"We will create a Label called mal_label which has the same original structure as the label above\n",
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"\n",
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"Notes:\n",
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"* Each label requires a valid feature schema id. We will assign it using our built in `assign_feature_schema_ids` method\n",
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"* the NDJsonConverter takes in a list of labels"
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]
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},
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"id": "10b19393-920a-45c8-9660-42d8c449b9c2",
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"outputId": "a93a39de-c8ed-402c-b834-304b1ba8854a"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'uuid': 'fde1945b-01df-43c2-807a-3f5fa20450f4',\n",
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" 'dataRow': {'id': 'ckzocppkf96r10z9q205151c3'},\n",
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" 'schemaId': 'ckzocpq4l9bw20z9s9bc70h29',\n",
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" 'classifications': [],\n",
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" 'bbox': {'top': 30.0, 'left': 30.0, 'height': 170.0, 'width': 170.0}}]"
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]
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},
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"execution_count": 6,
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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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"mal_label = Label(\n",
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" data=image_data,\n",
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" annotations = [rectangle_annotation]\n",
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")\n",
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"\n",
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"mal_label.assign_feature_schema_ids(ontology_builder.from_project(mal_project))\n",
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"\n",
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"ndjson_labels = list(NDJsonConverter.serialize([mal_label]))\n",
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"\n",
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"ndjson_labels"
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"id": "41d103bc-a5fd-4f0b-95f0-7e9bc59fbd07"
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},
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"source": [
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"Label import is very similar to model-assisted labeling. We will need to re-assign the feature schema before continuing, \n",
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"but we can continue to use our NDJSonConverter\n",
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"\n",
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"We will create a Label called li_label which has the same original structure as the label above"
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"Label import is very similar to model-assisted labeling. We will create a Label called li_label which has the same original structure as the label above"
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]
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},
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{
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"id": "c95716d5-a1ee-46fe-8dca-313ce10f104f",
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"outputId": "0e83d5d0-1f51-4903-c777-f9c331781656"
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'uuid': '7be8bb0b-39a2-44a5-96ab-5def3752811b',\n",
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" 'dataRow': {'id': 'ckzocppkf96r10z9q205151c3'},\n",
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" 'schemaId': 'ckzocpqv80ukp0z9l67cc6liv',\n",
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" 'classifications': [],\n",
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" 'bbox': {'top': 30.0, 'left': 30.0, 'height': 170.0, 'width': 170.0}}]"
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]
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},
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"execution_count": 9,
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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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"li_label = Label(\n",
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" data=image_data,\n",
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" annotations = [rectangle_annotation]\n",
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")\n",
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"\n",
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"li_label.assign_feature_schema_ids(ontology_builder.from_project(li_project))\n",
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"\n",
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"ndjson_labels = list(NDJsonConverter.serialize([li_label]))\n",
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"\n",
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"ndjson_labels"

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