Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use arpanl/Fine-Tuned_Model3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arpanl/Fine-Tuned_Model3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="arpanl/Fine-Tuned_Model3") 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/Fine-Tuned_Model3") model = AutoModelForImageClassification.from_pretrained("arpanl/Fine-Tuned_Model3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from arpanl/Fine-Tuned_Model3: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/arpanl/Fine-Tuned_Model3/resolve/main/training_args.bin
- Command line
-
hf download hf://arpanl/Fine-Tuned_Model3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/arpanl/Fine-Tuned_Model3/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 999010c2c916d591aa7130896bef9e86e781cf24ece716f36c812f6a4f101340
- Size of remote file:
- 4.92 kB
- SHA256:
- 139151df16906c7bd35710edb5fe55a0046730ba0d50cd7eb8e651e9d49f769e
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