Instructions to use shubhamWi91/train9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train9 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train9")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train9") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shubhamWi91/train9: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/shubhamWi91/train9/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train9/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train9/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 11171942fb9f84ff276b6c78ebf4286d66cfa697b9aba5c1e44af8d8e5fecf77
- Size of remote file:
- 243 MB
- SHA256:
- cc8e81581c2f52d80e9e1e46f4443b5f76cf6f8045ae8baa3da32b295bcff043
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