Instructions to use expertlearning/vision-perceiver-fourier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use expertlearning/vision-perceiver-fourier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="expertlearning/vision-perceiver-fourier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForImageClassification tokenizer = AutoTokenizer.from_pretrained("expertlearning/vision-perceiver-fourier") model = AutoModelForImageClassification.from_pretrained("expertlearning/vision-perceiver-fourier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from expertlearning/vision-perceiver-fourier: direct link, hf CLI and curl.
- Browser
- Download file 194 MB
-
https://huggingface.co/expertlearning/vision-perceiver-fourier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://expertlearning/vision-perceiver-fourier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/expertlearning/vision-perceiver-fourier/resolve/main/pytorch_model.bin
194 MB
- Xet hash:
- 2c6d94840b0a24ddf5ec729c8bfb02c60a8e04aef6442f3a3f6a5c92261b7e37
- Size of remote file:
- 194 MB
- SHA256:
- 8e4ce985e93368572a93b64cdcb2ee38cd44ba9560f607ae5f0231f303720055
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.