swin-s3-base-pascal_test

🦢 Swin S3 Base (224) - Pascal VOC

A Swin S3 Base model fine-tuned on the Pascal VOC 2012 dataset for multi-class image classification.

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🧠 Model Details

• Architecture: Swin S3 Base (224x224 input size)
• Pretrained on: ImageNet-1k
• Fine-tuned on: Pascal VOC 2012
• Framework: PyTorch (timm implementation)
• Format: safetensors

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🎯 Intended Use

• Primary task: Image classification of natural scenes featuring objects from 20 Pascal VOC categories.
• Users: Researchers, developers working on computer vision applications, model benchmarking.
• Not intended for: Real-time decision making in critical applications (e.g., autonomous vehicles, medical diagnosis).

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⚠️ Limitations and Ethical Considerations

• Biases: The model inherits biases present in Pascal VOC, such as underrepresentation of certain object types, contexts, or demographics. It may perform poorly on out-of-distribution samples.
• Ethical Use: Avoid using this model for applications that could reinforce harmful stereotypes, cause social harm, or violate privacy (e.g., surveillance).
• Transparency: This model is shared for research and educational use and should not be deployed without thorough fairness, robustness, and security evaluations.

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⚙️ Training Details

• Training library: timm + PyTorch
• Epochs: 5
• Batch size: 16
• Optimizer: AdamW
• Learning rate: 5e-5
• Scheduler: Cosine Annealing
• Loss function: BCE
• Hardware: 1x NVIDIA A100 on Google Colab Pro

ℹ️ Link to experiment tracking dashboard (e.g., Weights & Biases) (optional)

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📊 Evaluation Results

Evaluated on Pascal VOC 2012 test set:

Note: Evaluation performed using standard multi-class metrics. Model was not evaluated on cross-domain generalization.

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📚 Dataset

• Name: Pascal VOC 2012
• License: Creative Commons Attribution 4.0 International
• Labels: 20 object categories (person, car, dog, etc.)
• Split used: Training for fine-tuning, validation for evaluation

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💾 Files in This Repository

• model.safetensors: Model weights
• README.md: Model card (this file)

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🔗 Citations

@inproceedings{liu2021swin,
  title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author={Liu, Ze and Lin, Yutong and Cao, Yu and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
  booktitle={ICCV},
  year={2021}
}

@article{Everingham10,
  author = {Everingham, M. and Van Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
  title = {The Pascal Visual Object Classes (VOC) Challenge},
  journal = {IJCV},
  year = {2010},
  volume = {88},
  number = {2},
  pages = {303--338}
}