---
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.