Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use mshuggingface/swin-tiny-patch4-window7-224-ms-test1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mshuggingface/swin-tiny-patch4-window7-224-ms-test1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mshuggingface/swin-tiny-patch4-window7-224-ms-test1") 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("mshuggingface/swin-tiny-patch4-window7-224-ms-test1") model = AutoModelForImageClassification.from_pretrained("mshuggingface/swin-tiny-patch4-window7-224-ms-test1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mshuggingface/swin-tiny-patch4-window7-224-ms-test1: direct link, hf CLI and curl.
- Browser
- Download file 110 MB
-
https://huggingface.co/mshuggingface/swin-tiny-patch4-window7-224-ms-test1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mshuggingface/swin-tiny-patch4-window7-224-ms-test1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mshuggingface/swin-tiny-patch4-window7-224-ms-test1/resolve/main/pytorch_model.bin
110 MB
- Xet hash:
- 9b6bcb1b2dc67daca66e927f78fb307cc5632ab1fbf6daca0240af770acfee6b
- Size of remote file:
- 110 MB
- SHA256:
- 733481c132672f82834df0158f8e2b4423c0451b231363f93dbda912f0eeadbe
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.