Instructions to use mrisdi/asl_classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrisdi/asl_classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mrisdi/asl_classification") 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("mrisdi/asl_classification") model = AutoModelForImageClassification.from_pretrained("mrisdi/asl_classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from mrisdi/asl_classification: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/mrisdi/asl_classification/resolve/main/training_args.bin
- Command line
-
hf download hf://mrisdi/asl_classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/mrisdi/asl_classification/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 3fb7f67ee4f75f21e1984f658aec2f861cb3bb701913cbcb73510b1acd17f224
- Size of remote file:
- 5.24 kB
- SHA256:
- 35e0d66e311772cc7ef7538399a60ca6cae5684305943231722e7b81abda208b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.