Instructions to use shubhamWi91/train4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shubhamWi91/train4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="shubhamWi91/train4")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("shubhamWi91/train4") model = AutoModelForObjectDetection.from_pretrained("shubhamWi91/train4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from shubhamWi91/train4: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/shubhamWi91/train4/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://shubhamWi91/train4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/shubhamWi91/train4/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- ddce9dfbe9f16341b646d0c345f6d5c7b35f572568fb96dcf656d4d7c2b12f87
- Size of remote file:
- 167 MB
- SHA256:
- f14bacdcbc8493735644beecf1f13a948acca96b4c8316cdb7ba4de68068d0b6
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