Instructions to use microsoft/swin-tiny-patch4-window7-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/swin-tiny-patch4-window7-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/swin-tiny-patch4-window7-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224") model = AutoModelForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (8cb4bfdacc2c859409e5764f6f5d26ea1c4a35e5) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>d00d478
-
Update README.md
466da6d
-
Add TF model
83d40fb
-
Update preprocessor_config.json
896e18f -
Create README.md
55e81e3
-
Upload pytorch_model.bin with git-lfs
d9a10cd -
Upload preprocessor_config.json
66565bf -
Upload config.json
391c3df -
initial commit
fa79432