Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use amiqinayat/swin-tiny-patch4-window7-224-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amiqinayat/swin-tiny-patch4-window7-224-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amiqinayat/swin-tiny-patch4-window7-224-finetuned") 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("amiqinayat/swin-tiny-patch4-window7-224-finetuned") model = AutoModelForImageClassification.from_pretrained("amiqinayat/swin-tiny-patch4-window7-224-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f356a411965dfe382ac929337ab73f24c075f3c980e1c7ef9611caf1c55448aa
- Size of remote file:
- 4.03 kB
- SHA256:
- b6a98597527d0ba72f357d360c81b299a2134a1ece0f2a57cddc436e64710e31
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