Image Classification
Transformers
Safetensors
softmasked_selective_vit
vision-transformer
efficient-transformer
selective-attention
knowledge-distillation
computer-vision
custom_code
Instructions to use XAFT/SM-Selective-ViT-Tiny-Tall-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XAFT/SM-Selective-ViT-Tiny-Tall-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="XAFT/SM-Selective-ViT-Tiny-Tall-224", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("XAFT/SM-Selective-ViT-Tiny-Tall-224", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Add citation
Browse files
README.md
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## Acknowledgments
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We thank the TPU Research Cloud program for providing cloud TPUs that were used to build and train the models for our extensive experiments.
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## Acknowledgments
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We thank the TPU Research Cloud program for providing cloud TPUs that were used to build and train the models for our extensive experiments.
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```bibtex
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@article{TOULAOUI2026115151,
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title = {Efficient vision transformers via patch selective soft-masked attention and knowledge distillation},
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journal = {Applied Soft Computing},
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pages = {115151},
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year = {2026},
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issn = {1568-4946},
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doi = {https://doi.org/10.1016/j.asoc.2026.115151},
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url = {https://www.sciencedirect.com/science/article/pii/S1568494626005995},
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author = {Abdelfattah Toulaoui and Hamza Khalfi and Imad Hafidi},
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keywords = {Vision transformer, Patch selection, Soft masking, Efficient inference}
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}
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```
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