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 pytorch_model.bin from garykai/rbc_binary_ivo_v2: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/garykai/rbc_binary_ivo_v2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://garykai/rbc_binary_ivo_v2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/garykai/rbc_binary_ivo_v2/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 43b19ad2da3b8a7c7b379a0aa72079614478544312c294ceb6cbe3a076e69b01
- Size of remote file:
- 343 MB
- SHA256:
- d39ae6e2fcd1bf615cc26c5c51c1227473515be0d4d4498260e3cb73cf9723ac
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.