Instructions to use sergiopaniego/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sergiopaniego/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="sergiopaniego/detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("sergiopaniego/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("sergiopaniego/detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from sergiopaniego/detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/sergiopaniego/detr_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://sergiopaniego/detr_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/sergiopaniego/detr_finetuned_cppe5/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- 21bd0b68983a9376925d24b21373d5737259dd5bfa4a57fea57335e895466de9
- Size of remote file:
- 5.18 kB
- SHA256:
- 99e931a1db91cede07abbd2741482f0ef765b16e26e86c73ad09807ab8c186b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.