Instructions to use Tushar86/yolo-testing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tushar86/yolo-testing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Tushar86/yolo-testing") 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("Tushar86/yolo-testing") model = AutoModelForImageClassification.from_pretrained("Tushar86/yolo-testing", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tushar86/yolo-testing: direct link, hf CLI and curl.
- Browser
- Download file 348 MB
-
https://huggingface.co/Tushar86/yolo-testing/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tushar86/yolo-testing/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tushar86/yolo-testing/resolve/main/pytorch_model.bin
348 MB
- Xet hash:
- 959b649348345822726d276744a0f7ec69d28d080cf614bd2af6b41e05d5c084
- Size of remote file:
- 348 MB
- SHA256:
- 565db7d472403211008b9e4a474988cc3dba64b0ba830001e6daf6cd09372f19
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