| --- |
| model_name: DGGD PACS final checkpoints |
| library_name: pytorch |
| tags: |
| - domain-generalization |
| - image-classification |
| - pacs |
| --- |
| # DGGD PACS final checkpoints |
|
|
| Code, environment, protocol and results: https://github.com/MrHeatcliff/DGGD |
|
|
| 360 completed final checkpoints: 48 tuned ERM runs (4 backbones × 4 domains × 3 seeds), plus 312 runs from 26 other algorithms (ResNet50 AugMix, default hyperparameters). All are selected on source-domain validation, not target oracle. No incomplete or smoke runs, intermediate Optuna trial weights, training data or optimizer-resume guarantee. |
|
|
| See `manifest.json` for paths, SHA256, sizes, source validation, target accuracy and selected update. Domain order: art_painting, cartoon, photo, sketch. Seeds: 100, 101, 102. |
| |
| ## Loading |
| |
| Use the matching DGGD code and package environment; these are PyTorch state dictionaries, not Transformers models. The backbone initially requires cached pretrained weights and DINOv2 code where applicable. `best.pt` contains `model_dict`, `hparams`, and task metadata. Build `domainbed.algorithms.ERM` for ERM, or `scripts.pacs_algorithms.compat.algorithm_class(name)` for other algorithms, then load `model_dict` and call `eval()`. Fishr requires the compatibility class. Only load pickle checkpoints from a trusted source. |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| import torch |
| path = hf_hub_download(repo_id="datTrantien17/DGGD-checkpoints", filename="PATH_FROM_MANIFEST") |
| checkpoint = torch.load(path, map_location="cpu", weights_only=False) |
| ``` |
| |
| These checkpoints inherit the applicable terms of their original pretrained backbones and code; see DINOv2, torchvision and timm model documentation. This upload does not claim a new blanket license over third-party weights. Evaluation uses the custom protocol documented in the linked repository; ERM and other algorithms have different tuning budgets and numeric precision. |
| |
