Instructions to use fimbit/detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fimbit/detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="fimbit/detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("fimbit/detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("fimbit/detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from fimbit/detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 5.18 kB
-
https://huggingface.co/fimbit/detr_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://fimbit/detr_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/fimbit/detr_finetuned_cppe5/resolve/main/training_args.bin
5.18 kB
- Xet hash:
- d63f7f0c7b16fc76ff20c7bedb81ac8c28d27dde7499eeee63b33201f2d7a417
- Size of remote file:
- 5.18 kB
- SHA256:
- 6720a9280884929037e1f5b0ded79f339b7c50a12e3cfe768225dceeb388b73e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.