Instructions to use bismanwz/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bismanwz/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="bismanwz/detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("bismanwz/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("bismanwz/detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from bismanwz/detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/bismanwz/detr_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://bismanwz/detr_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/bismanwz/detr_finetuned_cppe5/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- a4608adcd8e0f2193e3afcb277fdc271dc2633012bd4fb2767ec49340a2f45a9
- Size of remote file:
- 5.37 kB
- SHA256:
- c4dc34f5671761c5792bcae28e8ba5653c4ea41383b2c325dddf3df7cb86c15f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.