Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jeraldflowers/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeraldflowers/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jeraldflowers/vit_model") 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("jeraldflowers/vit_model") model = AutoModelForImageClassification.from_pretrained("jeraldflowers/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jeraldflowers/vit_model: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/jeraldflowers/vit_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jeraldflowers/vit_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jeraldflowers/vit_model/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 80bb0d3366963d7708303c9df3151a5c37bae682ebde90d232eb76295eec5507
- Size of remote file:
- 343 MB
- SHA256:
- 148ed923f10817da5d76250ea269029ab56601ae72e58883ca509dd35fbd476a
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