Instructions to use facebook/regnet-y-040 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/regnet-y-040 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="facebook/regnet-y-040") 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("facebook/regnet-y-040") model = AutoModelForImageClassification.from_pretrained("facebook/regnet-y-040", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from facebook/regnet-y-040: direct link, hf CLI and curl.
- Browser
- Download file 82.9 MB
-
https://huggingface.co/facebook/regnet-y-040/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://facebook/regnet-y-040/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/facebook/regnet-y-040/resolve/main/flax_model.msgpack
82.9 MB
- Xet hash:
- f2e0bcf6ebe1573388181779711f7a84fd498906c434698a9458b5d4a4ba106d
- Size of remote file:
- 82.9 MB
- SHA256:
- 4e2a94ca56e86c8a9498880b0e154bcbd33e331c3f15a206f46ca4674050deda
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