Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use derhuli/vit-base-beans with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use derhuli/vit-base-beans with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="derhuli/vit-base-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("derhuli/vit-base-beans") model = AutoModelForImageClassification.from_pretrained("derhuli/vit-base-beans", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from derhuli/vit-base-beans: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/derhuli/vit-base-beans/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://derhuli/vit-base-beans/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/derhuli/vit-base-beans/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 299aa5a3f7fabe11c439bba3bef4c1da9cd0aa7313954e41caeb9fbff3f5e40c
- Size of remote file:
- 343 MB
- SHA256:
- c14b496b3e9f68cef2faffd642be2f38189ecb0e8a561ad8f51a8dbca6422ee6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.