tlc.integration.detectron2.register_coco_instances#
A drop-in replacement for detectron2.data.datasets.register_coco_instances
Module Contents#
Functions#
Function  | 
Description  | 
|---|---|
Register a COCO dataset in Detectron2’s standard format.  | 
API#
- tlc.integration.detectron2.register_coco_instances.register_coco_instances(name: str, metadata: dict, json_file: str, image_root: str | None, revision_url: str = '', project_name: str = '') None#
 Register a COCO dataset in Detectron2’s standard format.
This method works as a drop-in replacement for detectron2.data.datasets.register_coco_instances.
- References:
 
The original function reads the json file and uses
pycocoapito construct a list of dicts which are then registered under the keynamein detectron’sDatasetCatalog.These dicts have the following format:
{ "file_name": "COCO_train2014_000000000009.jpg", "height": 480, "width": 640, "image_id": 9, "annotations": [ { "bbox": [97.84, 12.43, 424.93, 407.73], "bbox_mode": 1, "category_id": 16, "iscrowd": 0, "segmentation": [[...]] }, ... ] }
This function also registers a list of dicts under the key
namein detectron’sDatasetCatalog, but before the data is generated, a TLCTable is resolved. The first time the function is called with a given signature, a 3LC table is created. On subsequent calls, the table replaced with the most recent descendant of the root table. If the resolved table contains aweightcolumn, this value will be sent along in the list of dicts.- Parameters:
 name – the name that identifies a dataset, e.g. “coco_2014_train”.
metadata – extra metadata associated with this dataset.
json_file – path to the json instance annotation file.
image_root – directory which contains all the images.
Noneif the file_name contains a complete path.revision_url – url to a specific revision of the table. If not provided, the latest revision will be used. If the revision is not a descendant of the initial table, an error will be raised.
- Returns:
 None