Instructions to use Jesse020202/detr_finetuned_cppe5_comparison with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jesse020202/detr_finetuned_cppe5_comparison with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Jesse020202/detr_finetuned_cppe5_comparison")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Jesse020202/detr_finetuned_cppe5_comparison") model = AutoModelForObjectDetection.from_pretrained("Jesse020202/detr_finetuned_cppe5_comparison", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jesse020202/detr_finetuned_cppe5_comparison: direct link, hf CLI and curl.
- Browser
- Download file 5.78 kB
-
https://huggingface.co/Jesse020202/detr_finetuned_cppe5_comparison/resolve/main/training_args.bin
- Command line
-
hf download hf://Jesse020202/detr_finetuned_cppe5_comparison/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jesse020202/detr_finetuned_cppe5_comparison/resolve/main/training_args.bin
5.78 kB
- Xet hash:
- 1301b4d7389035c9de348f1015bd28f20cb651b41b95e621d7c9abe06dfb9650
- Size of remote file:
- 5.78 kB
- SHA256:
- 793d0613a6930d27d6920121a8ffb51d638d354aedeb282704cc883dca8e1fbf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.