Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use ravipratap366/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 ravipratap366/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="ravipratap366/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("ravipratap366/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification") model = AutoModelForImageClassification.from_pretrained("ravipratap366/swin-tiny-patch4-window7-224-Mid-NonMidMarket-Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a91a691c430b6cda6fb3bf359a22b73cb0248c16343bec4886a1235c6a03723
- Size of remote file:
- 5.18 kB
- SHA256:
- 90eccb18a4f095b110820a81bde666017369f523bd4042f1ef2f0e3fab6f70be
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