Instructions to use semjon00/detr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use semjon00/detr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="semjon00/detr")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("semjon00/detr") model = AutoModelForObjectDetection.from_pretrained("semjon00/detr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from semjon00/detr: direct link, hf CLI and curl.
- Browser
- Download file 4.34 kB
-
https://huggingface.co/semjon00/detr/resolve/main/training_args.bin
- Command line
-
hf download hf://semjon00/detr/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/semjon00/detr/resolve/main/training_args.bin
4.34 kB
- Xet hash:
- 0b92e4e4ba0a143cb43ca3e946f5c9c42fd0e11fcf73d4367a84d66103fcfe16
- Size of remote file:
- 4.34 kB
- SHA256:
- 03c88de70bc7d9620f8c322b746e8ce31b60df20b4726cd1a4f2744d449beb01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.