shvit_s3-cifar

shvit_s3 Fine-tuned on CIFAR-100

This model is a fine-tuned version of SHViT (Single-Head Vision Transformer) on the CIFAR-100 dataset.

SHViT is from the CVPR 2024 paper: SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design by Seokju Yun and Youngmin Ro.

Model Description

• Base Model: shvit_s3
• Fine-tuned Dataset: CIFAR-100
• Number of Classes: 100
• Input Resolution: 224x224
• Framework: PyTorch / timm

Performance

• Test Accuracy (100% data): 68.59%
• Data Efficiency Score: 0.331
• Data for 90% Performance: 100.0%

Dataset

CIFAR-100: 100-class image classification dataset

• Classes: 100
• Image Size: 32x32 → 224x224 (resized)

Training Details

This model was trained as part of a comprehensive analysis comparing SHViT with baseline models (DeiT-Tiny, MobileNetV2) across multiple dimensions:

• Robustness to corruptions (noise, blur, weather effects)
• Data efficiency across different training data fractions
• Geometric invariance (rotation, crop, color changes)
• Domain adaptation capabilities
• Representation similarity analysis

Training configuration:

• Optimizer: AdamW
• Learning rate schedule: Cosine decay
• Augmentation: RandAugment, Random Erasing
• Input size: 224×224

Usage

import torch
from timm import create_model

# Load model
model = create_model('shvit_s3', num_classes=100, pretrained=False)

# Load checkpoint
checkpoint = torch.hub.load_state_dict_from_url(
    'hf://YOUR_USERNAME/shvit_s3-cifar/checkpoint_99.pth'
)
model.load_state_dict(checkpoint['model'])
model.eval()

# Use for inference
# (your image preprocessing code here)

Or use with Hugging Face Hub:

from huggingface_hub import hf_hub_download
import torch

# Download checkpoint
checkpoint_path = hf_hub_download(
    repo_id="YOUR_USERNAME/shvit_s3-cifar",
    filename="checkpoint_99.pth"
)

# Load model (requires timm and the SHViT model definition)
checkpoint = torch.load(checkpoint_path)
# ... load into your model

Analysis Repository

This model is part of a comprehensive analysis project. Full analysis code, scripts, and additional models available at:

• GitHub: [Your GitHub Repository]
• Paper/Report: [If available]

Analysis Scripts Include:

• Learning curve analysis across data fractions
• Robustness evaluation under various corruptions
• Geometric invariance testing (rotation, crop, color)
• Domain shift and transfer learning experiments
• Representation similarity (CKA, CCA) analysis
• Gradient-based saliency visualizations

Citation

If you use this model, please cite the original SHViT paper:

@inproceedings{yun2024shvit,
  author={Yun, Seokju and Ro, Youngmin},
  title={SHViT: Single-Head Vision Transformer with Memory Efficient Macro Design},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={5756--5767},
  year={2024}
}

And if you found this fine-tuned model or analysis useful:

@misc{shvit_s3_cifar,
  author = {Your Name},
  title = {shvit_s3 Fine-tuned on CIFAR-100},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/YOUR_USERNAME/shvit_s3-cifar}},
}

Collaborators

This work was done in collaboration with:

• Vishal V
• Priyal Garg

License

This model follows the MIT license. The original SHViT implementation is also under MIT license.

Acknowledgments

• Original SHViT authors: Seokju Yun and Youngmin Ro
• Built using timm
• Trained models and analysis scripts available in our repository