Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use arpanl/custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/custom") 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("arpanl/custom") model = AutoModelForImageClassification.from_pretrained("arpanl/custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arpanl/custom: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/arpanl/custom/resolve/main/training_args.bin
- Command line
-
hf download hf://arpanl/custom/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arpanl/custom/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- c50858d751baaa9ea658039bfd93362141b672801b6c6498f8362c6f09729f9e
- Size of remote file:
- 4.54 kB
- SHA256:
- 70e9974c164b74b1bc77183dd510712b55b6a09d575576e0c816e2f5f36666e5
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