Image Classification
Transformers
TensorBoard
Safetensors
vit
LMH
3_class
ViT
Generated from Trainer
Instructions to use 2todeux/ViT_beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use 2todeux/ViT_beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="2todeux/ViT_beans") 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("2todeux/ViT_beans") model = AutoModelForImageClassification.from_pretrained("2todeux/ViT_beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from 2todeux/ViT_beans: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/2todeux/ViT_beans/resolve/main/training_args.bin
- Command line
-
hf download hf://2todeux/ViT_beans/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/2todeux/ViT_beans/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- b202c8ce9b8fc8d6cfcedca4e96c0f225b637e9e3b022b884fccd03a85bc50ab
- Size of remote file:
- 5.18 kB
- SHA256:
- 464044988a160133bcb6a608c502d0640cca65784a7eefb701c636128213453e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.