Instructions to use shubhamWi91/train3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train3")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train3") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shubhamWi91/train3: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/shubhamWi91/train3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train3/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 0dcdcb050d68648329c6591cee15f16742359fea2f1a10b11e4950e2d03d7d69
- Size of remote file:
- 243 MB
- SHA256:
- e2a54694375b4fe30478337f828fe9bcc3088ae73f93afba87b5711e71c4c354
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