Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
customManuel
Generated from Trainer
custom_code
Instructions to use Manu8/vit_cifar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manu8/vit_cifar with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Manu8/vit_cifar", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageClassification model = AutoModelForImageClassification.from_pretrained("Manu8/vit_cifar", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Manu8/vit_cifar: direct link, hf CLI and curl.
- Browser
- Download file 10.9 MB
-
https://huggingface.co/Manu8/vit_cifar/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Manu8/vit_cifar/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Manu8/vit_cifar/resolve/main/pytorch_model.bin
10.9 MB
- Xet hash:
- 9f0a7526bba8bb78b057e0aac596992d426a3ba6a8f655bae5198288d2c8da92
- Size of remote file:
- 10.9 MB
- SHA256:
- acc4b878257413dc4274c96ac81568b5ce3a156797d04921cd1469052dc011de
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