Instructions to use rjhugs/modelStructure_TT_V4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjhugs/modelStructure_TT_V4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="rjhugs/modelStructure_TT_V4")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("rjhugs/modelStructure_TT_V4") model = AutoModelForObjectDetection.from_pretrained("rjhugs/modelStructure_TT_V4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rjhugs/modelStructure_TT_V4: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/rjhugs/modelStructure_TT_V4/resolve/main/training_args.bin
- Command line
-
hf download hf://rjhugs/modelStructure_TT_V4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rjhugs/modelStructure_TT_V4/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 5bfab65af4636e92f19a34cc071cf3043e3202a323126138529c876aa192fdca
- Size of remote file:
- 5.37 kB
- SHA256:
- 49338fce3f3e0c0642a4df66fc3e3b8d598cc7116d7e5275786a5ebd178be04d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.