Instructions to use JuudasMooses/detr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JuudasMooses/detr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="JuudasMooses/detr")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("JuudasMooses/detr") model = AutoModelForObjectDetection.from_pretrained("JuudasMooses/detr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from JuudasMooses/detr: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/JuudasMooses/detr/resolve/main/training_args.bin
- Command line
-
hf download hf://JuudasMooses/detr/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/JuudasMooses/detr/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 31a8b8798e981d0871903aab67b830a0be6f26abb0377df54f7e1b88843cc96a
- Size of remote file:
- 4.92 kB
- SHA256:
- 47cac1b3adcb6df02d383ebb97e7861b4be98eb6a601838b0d99f880ac0d3065
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