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:
- 280c592d94c8b66e5de2bb7747195bb2b35a8548fbe182e21b6ec96d95231a69
- Size of remote file:
- 110 MB
- SHA256:
- 22f13f7e71b5aebdd36f48d51956004a1537c0624590b12e4c7ba44ec363ef5e
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