Instructions to use garykai/rbc_binary_ivo_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use garykai/rbc_binary_ivo_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="garykai/rbc_binary_ivo_v2") 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("garykai/rbc_binary_ivo_v2") model = AutoModelForImageClassification.from_pretrained("garykai/rbc_binary_ivo_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from garykai/rbc_binary_ivo_v2: direct link, hf CLI and curl.
- Browser
- Download file 3.57 kB
-
https://huggingface.co/garykai/rbc_binary_ivo_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://garykai/rbc_binary_ivo_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/garykai/rbc_binary_ivo_v2/resolve/main/training_args.bin
3.57 kB
- Xet hash:
- badf867e87423d26addfdfeb218071e8648cd07fc922e71e9ede0a0745278674
- Size of remote file:
- 3.57 kB
- SHA256:
- fd1aba3f96b675835165c178368d504b116a2797dc3e732248c2ca43e42aade3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.