Merged
Conversation
- adding missing docs for superbird
remove debug statements
AlexEMG
reviewed
Jul 6, 2025
-remove debug print statement
* all working * Update fasterRCNN.py - remove the extra debug statements
Member
Author
|
To be clear, this fixes the failing superanimal models except TVM + mobilenet; everything else works. |
1224a57 to
8af154b
Compare
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
There was a bug in
rc11that causes the nonhumanbodySA models to attempt to use thetorchvisionCOCO detector vs. the custom ones. This fixes that error.SA_TVM is not just fully functional, but it's a WIP for @MMathisLab
It also required a fix in dlclibrary, which needs merged before this: Update modelzoo_urls_pytorch.yaml DLClibrary#41
It also cleans up the GUI a bit to remove the un-needed video type button. The code automatically handles this anyhow.
It has two detectors for humans now as well:
"fasterrcnn_mobilenet_v3_large_fpn", "fasterrcnn_resnet50_fpn_v2"I did some speed profiling locally on my M2 Apple Chip macbook; but only using the CPU on a short video (so just consider relative perf. speed, not as top-end)
Results:
superanimal_quadrupedmodel + detector combinations onjasper-short.movwere successful.humanbodypassedbirdpassedfasterrcnn_resnet50_fpn_v2detector now works for all backbones (no failures, no fallback to torchvision COCO weights).Short Results
Extended Tests Passing
GIST for the testscript: https://gist.github.com/MMathisLab/6a4ba42af34b43b2d9b2be4040ee02cf
📊 Overall Summary
⏱️ Performance Statistics
🦄 SuperAnimal Quadruped
Success Rate: 9/9 (100.0%)
Models & Detectors
Average Time: 12.95s
👤 SuperAnimal Humanbody
Success Rate: 2/2 (100.0%)
Models & Detectors
Average Time: 22.09s
🐦 SuperAnimal Bird
Success Rate: 2/2 (100.0%)
Models & Detectors
Average Time: 3.36s
🔧 Technical Details
Output Files
All results saved to:
/Users/mackenzie/Desktop/testing/comprehensive_superanimal_tests/Test Videos
jasper-short.mov(5 frames)bio-very-short.mov(19 frames)bird.mov(14 frames)