Instructions to use Dukeb/detr-detection-PCBComponents with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dukeb/detr-detection-PCBComponents with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Dukeb/detr-detection-PCBComponents")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Dukeb/detr-detection-PCBComponents") model = AutoModelForObjectDetection.from_pretrained("Dukeb/detr-detection-PCBComponents", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Dukeb/detr-detection-PCBComponents: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/Dukeb/detr-detection-PCBComponents/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Dukeb/detr-detection-PCBComponents/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Dukeb/detr-detection-PCBComponents/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 05101fd39d867e6267566e0ff66ac53aab893c88a2a2c50f132942217c0d118d
- Size of remote file:
- 167 MB
- SHA256:
- 1ec5f806f66f35182ea6ee0d1cf5a39c6a6f1b1b198a3e3ba7a2566e278be87d
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