Instructions to use pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test") model = AutoModelForObjectDetection.from_pretrained("pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test/resolve/main/training_args.bin
- Command line
-
hf download hf://pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pylu5229/conditional-detr-resnet-50-uLED-obj-detect-test/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 6f6f533a2d7508d58e6f10f5f1e2bc5c7f0f86364f63545fa0eee205f3707ac0
- Size of remote file:
- 5.43 kB
- SHA256:
- 004fca448e2671679e4446bc5bece541b91ecf1bcab10fff589f51728863daaf
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