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 pytorch_model.bin from rjhugs/modelStructure_TT_V3: direct link, hf CLI and curl.
- Browser
- Download file 116 MB
-
https://huggingface.co/rjhugs/modelStructure_TT_V3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rjhugs/modelStructure_TT_V3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rjhugs/modelStructure_TT_V3/resolve/main/pytorch_model.bin
116 MB
- Xet hash:
- 40313c9a2f9c8027336756d7582bf2e64162f0a4a8018d579a90a640757bb762
- Size of remote file:
- 116 MB
- SHA256:
- 66b00d9e5a4d901abed3f9413bd1b2894f022b952da5ff408acc4801d12aff02
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