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 images/bald.jpg from mvaloatto/bald-or-not: direct link, hf CLI and curl.
- Browser
- Download file 32.2 kB
-
https://huggingface.co/mvaloatto/bald-or-not/resolve/main/images/bald.jpg
- Command line
-
hf download hf://mvaloatto/bald-or-not/images/bald.jpg
-
curl -L -o bald.jpg https://huggingface.co/mvaloatto/bald-or-not/resolve/main/images/bald.jpg
32.2 kB

- Xet hash:
- 7056950232f3dc9cf4887ab77d0424043608a703e425b9e873d95e80a11981d1
- Size of remote file:
- 32.2 kB
- SHA256:
- 0e7773db9cb917860386ee44d155c3dcec6e5366306216d3b9dc2c42e484c258
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.