Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use pthpth/ViTFineTuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pthpth/ViTFineTuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pthpth/ViTFineTuned") 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("pthpth/ViTFineTuned") model = AutoModelForImageClassification.from_pretrained("pthpth/ViTFineTuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pthpth/ViTFineTuned: direct link, hf CLI and curl.
- Browser
- Download file 3.31 kB
-
https://huggingface.co/pthpth/ViTFineTuned/resolve/main/training_args.bin
- Command line
-
hf download hf://pthpth/ViTFineTuned/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pthpth/ViTFineTuned/resolve/main/training_args.bin
3.31 kB
- Xet hash:
- f3ff74ad517d9dd0583eee79aaabe33ff611e4b788bc1697f6ba601d03f3c500
- Size of remote file:
- 3.31 kB
- SHA256:
- 25629e62085b1eb48fe8542c334d32c1b3021e58f9e927d1e63735b084a5587f
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