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 training_args.bin from Satish678/UIED_Transformer: direct link, hf CLI and curl.
- Browser
- Download file 3.9 kB
-
https://huggingface.co/Satish678/UIED_Transformer/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://Satish678/UIED_Transformer@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Satish678/UIED_Transformer/resolve/refs%2Fpr%2F1/training_args.bin
3.9 kB
- Xet hash:
- 1c49df531010514de132945453bb7e7687ed2fa58dee52f6ff90b83a1e9870b9
- Size of remote file:
- 3.9 kB
- SHA256:
- f3f38527d681f99d327384b3d9aa9caf2454fc774a6cab96ab465b477e27b9a5
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