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 training_args.bin from jeraldflowers/vit_model: direct link, hf CLI and curl.
- Browser
- Download file 3.38 kB
-
https://huggingface.co/jeraldflowers/vit_model/resolve/main/training_args.bin
- Command line
-
hf download hf://jeraldflowers/vit_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/jeraldflowers/vit_model/resolve/main/training_args.bin
3.38 kB
- Xet hash:
- 4ff2593189cfed0d09fb1f1a388a564efbba48defd17ca89cbffbe48fb9fceba
- Size of remote file:
- 3.38 kB
- SHA256:
- b9f1b4393b01c93fedb6520911438fe2bf4f9b36f7556667f1859fc9b4a567ac
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