README.md

---
license: bsd-3-clause
library_name: pytorch
tags: [image-classification, imagenet, imagenet-c, resnet50, robustness, tta]
pipeline_tag: image-classification
---

# TTA-ImageNet-ResNet50

Mirror of the standard **ImageNet-1K ResNet-50** source model used for
ImageNet-C test-time adaptation baselines.

This checkpoint is generated from torchvision
`ResNet50_Weights.IMAGENET1K_V1`. It matches the model definition used by
RobustBench's ImageNet-C `Standard_R50` entry:
`torchvision.models.resnet50(pretrained=True)` with ImageNet mean/std
normalization applied outside the backbone.

## Why This Model

- TENT's official example stack depends on RobustBench for datasets and
  pre-trained models.
- EATA reports ImageNet-C severity-5 results with ResNet-50.
- SAR evaluates ImageNet-C with ResNet-50 and ViT variants; the classic
  ResNet-50 backbone remains the baseline anchor.

For more recent "wild" or batch-size-1 settings, papers often add
ResNet-50-GN and ViT-B/LN variants. This repo uses the BN ResNet-50 as the
canonical ImageNet-C source checkpoint because it matches the original
TENT/EATA-style setting and exposes BN affine parameters for TENT.

## Model Details

- **Upstream equivalent**: RobustBench `Standard_R50` for ImageNet corruptions
- **Torchvision weights**: `ResNet50_Weights.IMAGENET1K_V1`
- **Arch**: `resnet50`
- **Params**: 25,557,032
- **Clean ImageNet val accuracy**: not evaluated
- **Input**: RGB image, resized/cropped to 224, then normalized with
  mean `[0.485, 0.456, 0.406]` and std `[0.229, 0.224, 0.225]`.

## Usage

```python
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from torchvision.models import resnet50

path = hf_hub_download("WNJXYK/TTA-ImageNet-ResNet50", "model.safetensors", revision="v1.0")
model = resnet50(weights=None, num_classes=1000)
model.load_state_dict(load_file(path))
```

Inside **TTA-Evaluation-Harness**:

```yaml
# configs/source_models/resnet50_imagenet.yaml
framework: torchvision_hf
arch:      resnet50
hf_repo:   WNJXYK/TTA-ImageNet-ResNet50
revision:  v1.0
```

## Citations

```bibtex
@inproceedings{he2016deep,
  title={Deep Residual Learning for Image Recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={CVPR}, year={2016}
}

@inproceedings{hendrycks2019benchmarking,
  title={Benchmarking Neural Network Robustness to Common Corruptions and Perturbations},
  author={Hendrycks, Dan and Dietterich, Thomas},
  booktitle={ICLR}, year={2019}
}

@inproceedings{croce2021robustbench,
  title={RobustBench: a standardized adversarial robustness benchmark},
  author={Croce, Francesco and Andriushchenko, Maksym and Sehwag, Vikash
           and Debenedetti, Edoardo and Flammarion, Nicolas and Chiang, Mung
           and Mittal, Prateek and Hein, Matthias},
  booktitle={NeurIPS Datasets and Benchmarks Track}, year={2021}
}
```