Image Classification
Transformers
Safetensors
English
siglip
Gesture
Classification
SigLIP2
19:Styles
Vision-Encoder
Instructions to use prithivMLmods/Hand-Gesture-19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Hand-Gesture-19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="prithivMLmods/Hand-Gesture-19") 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/Hand-Gesture-19") model = AutoModelForImageClassification.from_pretrained("prithivMLmods/Hand-Gesture-19", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from prithivMLmods/Hand-Gesture-19: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/prithivMLmods/Hand-Gesture-19/resolve/main/training_args.bin
- Command line
-
hf download hf://prithivMLmods/Hand-Gesture-19/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/prithivMLmods/Hand-Gesture-19/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 86d6658f1cf862c76aa2dafe7bb5130654fa854dd0a83832336576312518ae60
- Size of remote file:
- 5.3 kB
- SHA256:
- ece2f315761755eaa1563a5a7e3c0364fb0faa58002113aa00850f59d1176766
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.