Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use Sara5115/swin-tiny-patch4-window7-224-BlurClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sara5115/swin-tiny-patch4-window7-224-BlurClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Sara5115/swin-tiny-patch4-window7-224-BlurClassification") 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("Sara5115/swin-tiny-patch4-window7-224-BlurClassification") model = AutoModelForImageClassification.from_pretrained("Sara5115/swin-tiny-patch4-window7-224-BlurClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6abdf532b9232e9ea68459ba10f072f62adee391b40e7f33e56291143f4915ce
- Size of remote file:
- 220 MB
- SHA256:
- e32f73969c912b2e97726afa8cf19ef78831cc2d356334457dcba1c95d22e251
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.