Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification") model = AutoModelForImageClassification.from_pretrained("djbp/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f768b342d6c1a812b4f9948aa1aa139d783abc0235efb4d9dce60704d16d452b
- Size of remote file:
- 110 MB
- SHA256:
- 32dda7f87d8bd0cf189fd40d26dcffcbc7892ba65302349d38f25b9d112cb2e9
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