Image Classification
Transformers
TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use pylu5229/16_label_check_point with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pylu5229/16_label_check_point with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pylu5229/16_label_check_point") 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("pylu5229/16_label_check_point") model = AutoModelForImageClassification.from_pretrained("pylu5229/16_label_check_point", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pylu5229/16_label_check_point: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/pylu5229/16_label_check_point/resolve/main/training_args.bin
- Command line
-
hf download hf://pylu5229/16_label_check_point/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pylu5229/16_label_check_point/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- bb58a333f058be7cfce20c47d1afda9dd5ec58cb8fe985a78465352c7a8cfa4f
- Size of remote file:
- 5.43 kB
- SHA256:
- 6a8175a9ad6281337906169a5a7d648239e46227467b846a8a98a2327fc311cd
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