Instructions to use mvaloatto/bald-or-not with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mvaloatto/bald-or-not with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mvaloatto/bald-or-not") 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("mvaloatto/bald-or-not") model = AutoModelForImageClassification.from_pretrained("mvaloatto/bald-or-not", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from mvaloatto/bald-or-not: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/mvaloatto/bald-or-not/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://mvaloatto/bald-or-not/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/mvaloatto/bald-or-not/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- f7e8642f2ab3d0896be41459a756b9a29706d973a1b90eff76b9149c6afa6474
- Size of remote file:
- 343 MB
- SHA256:
- 7296596cc3706b1c6d2a0c3ab67f7a8ba9fde32ce76a94fa352311ce9b58b5bf
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