Jonas Mueller, Ulyana Tkachenko, Aditya Thyagarajan
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| cleanlab/cleanlab | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| generate_image | Ran | cleanlab/cleanlab/tests/test_object_detection.py code served (permissive licence) · get_code("5b9fde645fe4b803") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite powering sensitive systems like autonomous vehicles, object detection remains fairly brittle in part due to annotation errors that plague most real-world training datasets. We propose ObjectLab, a straightforward algorithm to detect diverse errors in object detection labels, including: overlooked bounding boxes, badly located boxes, and incorrect class label assignments. Object-Lab utilizes any trained object detection model to score the label quality of each image, such that mislabeled images can be automatically prioritized for label review/correction. Properly handling erroneous data enables training a better version of the same object detection model, without any change in existing modeling code. Across different object detection datasets (including COCO) and different models (including Detectron-X101 and Faster-RCNN), ObjectLab consistently detects annotation errors with much better precision/recall compared to other label quality scores.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.00832")
get_code_for_paper("2309.00832")
have("2309.00832")
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