update RTMPose SimCCPredictor: expose apply_softmax and fix visibility thresholding - #3306
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the original visibility thresholding `locs[vals <= 0] = 1` makes sense for unnormalized logits, but after softmaxing this becomes effectively a no-op. The current commit changes this to a reasonable threshold for the case when apply_softmax=True
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Pull request overview
This PR adjusts RTMPose’s SimCC decoding configuration and post-processing to make likelihood/visibility behavior reproducible across runs and to correct visibility masking when outputs are softmax-normalized.
Changes:
- Exposes
apply_softmax(and explicitly setsnormalize_outputs) in the default RTMPose config YAMLs to avoid silent behavior changes. - Updates
get_simcc_maximum()visibility masking by introducing a softmax-aware baseline threshold instead of<= 0.0.
Reviewed changes
Copilot reviewed 4 out of 4 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
deeplabcut/pose_estimation_pytorch/models/predictors/sim_cc.py |
Adjusts SimCC visibility masking thresholding when apply_softmax is enabled. |
deeplabcut/pose_estimation_pytorch/config/rtmpose/rtmpose_x.yaml |
Exposes apply_softmax and normalize_outputs for the SimCC predictor in the X config. |
deeplabcut/pose_estimation_pytorch/config/rtmpose/rtmpose_s.yaml |
Exposes apply_softmax and normalize_outputs for the SimCC predictor in the S config. |
deeplabcut/pose_estimation_pytorch/config/rtmpose/rtmpose_m.yaml |
Exposes apply_softmax and normalize_outputs for the SimCC predictor in the M config. |
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deruyter92
marked this pull request as draft
May 8, 2026 11:52
deruyter92
marked this pull request as ready for review
May 12, 2026 13:55
C-Achard
approved these changes
May 12, 2026
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Motivation
A regression analysis revealed that naive training results in reduced mAP in recent versions of DeepLabCut (only scores and mAP are affected; keypoint predictions remain the same, so mAR, RMSE are stable).
Cause:
The cause could be pinpointed to the change in default value
apply_softmax=Trueintroduced in #3078. While this change is indeed required (output predictions should by default be normalized) this introduced two problems:apply_softmax=Truewithdecode_beta=150produces a sharp function which does not work well for all datasets. Sinceapply_softmaxis not exposed in the default config yaml, this causes a silent change in performance.locs[vals <= 0.0] = -1effectively is a no-op for the now normalized output values in range 0 to 1)Changes:
apply_softmax=Trueis now exposed in the default config yaml, to facilitate reproducible benchmark results with frozen configs.Note that the current PR does not alter the default value for the decode_beta parameter (might need further discussion and systematic testing), but for the current benchmarking results (see figure above), a value ~0.2 worked better than 300, and fully restored the original performance of before #3078
Update: a quick hyperparameter analysis was performed with different values of
decode_beta. See figures at the bottom. A separate PR will be opened to address this