| --- |
| license: cc-by-nc-4.0 |
| library_name: timm |
| tags: |
| - pedestrian-attribute-recognition |
| - person-retrieval |
| - upar |
| --- |
| |
| # 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). |
|
|
