Instructions to use prithivMLmods/open-age-detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/open-age-detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/open-age-detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("prithivMLmods/open-age-detection") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/open-age-detection", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download checkpoint-595/optimizer.pt from prithivMLmods/open-age-detection: direct link, hf CLI and curl.
- Browser
- Download file 687 MB
-
https://huggingface.co/prithivMLmods/open-age-detection/resolve/main/checkpoint-595/optimizer.pt
- Command line
-
hf download hf://prithivMLmods/open-age-detection/checkpoint-595/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/prithivMLmods/open-age-detection/resolve/main/checkpoint-595/optimizer.pt
687 MB
- Xet hash:
- b16e0092db986401fd317d88ec9ec5b33f570803dfae53e8df164bde36cd3f24
- Size of remote file:
- 687 MB
- SHA256:
- d9e1dff26cde2e4232667782cedbc2a45949891a243aa15226141270a45d6fa9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.