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- adding missing docs for superbird
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Integrate the RTMPose_x human‐body model into the DeepLabCut model zoo, add a dedicated video inference pipeline using torchvision detectors, update configs, docs, tests, and GUI layout.
Copilot reviewed 24 out of 24 changed files in this pull request and generated 1 comment.
Show a summary per file| File | Description |
|---|---|
| tests/pose_estimation_pytorch/modelzoo/test_webapp.py | Include superanimal_humanbody in webapp tests |
| tests/pose_estimation_pytorch/modelzoo/test_modelzoo_utils.py | Include superanimal_humanbody in utils tests |
| test_superanimal_humanbody.py | New top-level script for human-body detector integration test |
| deeplabcut/pose_estimation_pytorch/modelzoo/utils.py | Add HF download and special handling for human-body configs |
| deeplabcut/pose_estimation_pytorch/runners/inference.py | Introduce TorchvisionDetectorInferenceRunner |
| deeplabcut/gui/tabs/modelzoo.py | Revamp GUI: thresholds, media selector, adaptation options |
deeplabcut/pose_estimation_pytorch/runners/inference.py:866
class TorchvisionDetectorInferenceRunner(InferenceRunner[BaseDetector]):
deeplabcut/gui/tabs/modelzoo.py:365
help_text = deeplabcut.superanimal_analyze_images.__doc__
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Excellent
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remove debug statements
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Major Changes:
This integrates the RTMPose model that we demo with the low-level API in this notebook fully into the model zoo.
For ease, this is a new function called deeplabcut/pose_estimation_pytorch/modelzoo/superanimal_humanbody_video_inference.py
it also works in the api call
It also updates the model-zoo tab for better handling of thresholds and more elegant layout:
Minor: