Image Classification
Transformers
PyTorch
TensorBoard
beit
Generated from Trainer
Eval Results (legacy)
Instructions to use ChasingMercer/beit-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChasingMercer/beit-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ChasingMercer/beit-base") 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("ChasingMercer/beit-base") model = AutoModelForImageClassification.from_pretrained("ChasingMercer/beit-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ChasingMercer/beit-base: direct link, hf CLI and curl.
- Browser
- Download file 347 MB
-
https://huggingface.co/ChasingMercer/beit-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ChasingMercer/beit-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ChasingMercer/beit-base/resolve/main/pytorch_model.bin
347 MB
- Xet hash:
- 5ae99bc5c7f0f2c87ec097fae036fe649f18cd23cc482265fd9a473e695ea2d2
- Size of remote file:
- 347 MB
- SHA256:
- 854ca81feb0187ec7249943dd157c6f1112aeb4176c930eecc945e161226dc96
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