Instructions to use Andron00e/ViTForImageClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Andron00e/ViTForImageClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Andron00e/ViTForImageClassification") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Andron00e/ViTForImageClassification") model = AutoModelForImageClassification.from_pretrained("Andron00e/ViTForImageClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Andron00e/ViTForImageClassification: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/Andron00e/ViTForImageClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Andron00e/ViTForImageClassification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Andron00e/ViTForImageClassification/resolve/refs%2Fpr%2F1/pytorch_model.bin
343 MB
- Xet hash:
- bd6d87b1b70aae7b4d06c1aa1644a35228d40acfb512867bc48d8efef2c89188
- Size of remote file:
- 343 MB
- SHA256:
- eeb6345552a4571d91195871d9ab76dfc7b1adc2cc098c31402fcff9ce22601d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.