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 training_args.bin from ChasingMercer/beit-base: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/ChasingMercer/beit-base/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://ChasingMercer/beit-base@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ChasingMercer/beit-base/resolve/refs%2Fpr%2F1/training_args.bin
3.45 kB
- Xet hash:
- a3968a9470a5a5ddc53c10f910cfde7f3e4ac1dd1858390e58f04be944dccc5b
- Size of remote file:
- 3.45 kB
- SHA256:
- 4db4e240149591526f4f8d5e7f027bd5535a567c423e475220896470465cfbfb
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