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:
- 86b8d04b91882123cdc775591a8dedf0b96d3eea24840f9814e13608b230504e
- Size of remote file:
- 110 MB
- SHA256:
- 99428fc6b955aed3ad634f7693fd24932bf8f32171c9f9f1fb3baf422ebbd712
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