Instructions to use nateraw/baked-goods with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nateraw/baked-goods with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nateraw/baked-goods") 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("nateraw/baked-goods") model = AutoModelForImageClassification.from_pretrained("nateraw/baked-goods", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nateraw/baked-goods: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/nateraw/baked-goods/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nateraw/baked-goods/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nateraw/baked-goods/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 5f30d85b221b4b9b6b4d694b3e486fd187a80440d1c27f51c12a9d14d3b7e281
- Size of remote file:
- 343 MB
- SHA256:
- fb3714a0379c159424e2fb64ecb0dc7f72bc7c062bfc4156d2e6e34e285c11b7
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