UPAR Challenge 2027 attribute models (team angadbajwa)
Trained weights for our UPAR Challenge 2027 entries (WACV 2027 Real-World Surveillance workshop): Track 2, attribute-based person retrieval (69.11 mADM on the test set), and Track 1, pedestrian attribute recognition (79.55).
Code, training recipe and instructions: https://github.com/angadbajwa23/upar2027
| File | Backbone (timm name) | Used in |
|---|---|---|
eva02L_lpft_ep6.pt |
eva02_large_patch14_224.mim_m38m, LP-FT |
Track 1, Track 2 |
clipL_metaclip_ep4.pt |
vit_large_patch14_clip_224.metaclip_2pt5b |
Track 1, Track 2 |
clipL_openai_ep4.pt |
vit_large_patch14_clip_224.openai |
Track 2 |
Each file is a checkpoint written by train.py (fp16 EMA state dict under state_dict) for the
model in model.py: backbone, dropout and a linear layer with 40 sigmoid outputs, at input size 252×126.
make_submission.py in the code repository turns them into the Codabench submissions.
SHA-256 checksums are in weights.sha256 in the code repository.
License
Non-commercial research use only. The models are trained on the UPAR annotations (CC BY-NC-SA) and the PA100k, PETA and Market-1501 images, and one of them starts from MetaCLIP weights (CC BY-NC 4.0).
- Downloads last month
- -