Instructions to use shubhamWi91/train2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train2")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train2") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shubhamWi91/train2: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/shubhamWi91/train2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train2/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- f8e9e14509d158f7b55736a38b96e8019e8f062002ee92e7b8d7e982d23b35f7
- Size of remote file:
- 167 MB
- SHA256:
- ed29457e6db77e7da9cc1fffb0cbb98f90742290888b0da37d7825f1999c1125
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