Once your COCO file is verified, you're ready to import it to your model customization project. Purpose = Purpose.TRAINING # or Purpose.EVALUATIONĬheck_coco_annotation_file(json.loads(coco_file_path.read_text()), annotation_kind, purpose) from cognitive_service_vision_model_customization_python_samples import check_coco_annotation_file, AnnotationKind, PurposeĬoco_file_path = pathlib.Path("")Īnnotation_kind = AnnotationKind.MULTICLASS_CLASSIFICATION # or AnnotationKind.OBJECT_DETECTION You can either enter this code in a Python script, or run the Jupyter Notebook on a compatible platform. Then, run the following python code to check the file's format. First, install the python samples package from the command line: pip install cognitive-service-vision-model-customization-python-samples This notebook demonstrates how to check if the format of your annotation file is correct. Java is a registered trademark of Oracle and/or its affiliates.Contents of check_coco_annotation.ipynb. For details, see the Google Developers Site Policies. 'label': ClassLabel(shape=(), dtype=int64, num_classes=133),Įxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. 'panoptic_image/filename': Text(shape=(), dtype=string), 'panoptic_image': Image(shape=(None, None, 3), dtype=uint8), 'label': ClassLabel(shape=(), dtype=int64, num_classes=80), 'bbox': BBoxFeature(shape=(4,), dtype=float32), 'image/filename': Text(shape=(), dtype=string), 'image': Image(shape=(None, None, 3), dtype=uint8), Title = Ĭonfig description: This version contains images, bounding boxes and * Coco defines 91Ĭlasses but the data only uses 80 classes. The test split don't have any annotations (only images). *Ĭoco 20 uses the same images, but different train/val/test splits * Note: * Some images from the train and validation sets don't have annotations.
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