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:
- 20f056274cd0066475dda5b5cf6c2bf5ba6a39f2b4ca96b4203cc0df8bae7715
- Size of remote file:
- 110 MB
- SHA256:
- 4f49279f8e18f836a90ac3bfbbc284d9a15c439e27cfbb7ff28f68e0059ca49d
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