Instructions to use Satish678/UIED_Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Satish678/UIED_Transformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Satish678/UIED_Transformer")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Satish678/UIED_Transformer") model = AutoModelForObjectDetection.from_pretrained("Satish678/UIED_Transformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Satish678/UIED_Transformer: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/Satish678/UIED_Transformer/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Satish678/UIED_Transformer/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Satish678/UIED_Transformer/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 9917df3da983a24aa8d4c1bf287f1ab71d35d2ee887c9791c0173a1a9c7ec92a
- Size of remote file:
- 167 MB
- SHA256:
- f39040a5d96dabec65b665bef2b95ecd90f3754e667be4d743cb5d2e04659aa2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.