Instructions to use shubhamWi91/train50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train50")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train50") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train50", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shubhamWi91/train50: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/shubhamWi91/train50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train50/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 122d8c6454b392a0a320d5a9b4792e761310aeb054afa1d9c585816b5e8308ab
- Size of remote file:
- 243 MB
- SHA256:
- 53bc7fb8e4d7d483d5f9b789caa33f1cfe7a2c2968fc798213aef9a2ca6d03d2
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