Instructions to use rjhugs/modelStructure_TT_V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rjhugs/modelStructure_TT_V3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="rjhugs/modelStructure_TT_V3")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("rjhugs/modelStructure_TT_V3") model = AutoModelForObjectDetection.from_pretrained("rjhugs/modelStructure_TT_V3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rjhugs/modelStructure_TT_V3: direct link, hf CLI and curl.
- Browser
- Download file 4.54 kB
-
https://huggingface.co/rjhugs/modelStructure_TT_V3/resolve/main/training_args.bin
- Command line
-
hf download hf://rjhugs/modelStructure_TT_V3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rjhugs/modelStructure_TT_V3/resolve/main/training_args.bin
4.54 kB
- Xet hash:
- 30404b36014ab565fefb68bd3b7ca034467ae37ba1997951c6c05ecbc0eb176d
- Size of remote file:
- 4.54 kB
- SHA256:
- 1bd8a68a1b6d50aa8bd0298db4a7e4f73a42f65b234c388be14f19cd6294ab59
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