Instructions to use bpdevai/test_detr_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bpdevai/test_detr_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="bpdevai/test_detr_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("bpdevai/test_detr_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("bpdevai/test_detr_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-321/optimizer.pt from bpdevai/test_detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/bpdevai/test_detr_finetuned_cppe5/resolve/main/checkpoint-321/optimizer.pt
- Command line
-
hf download hf://bpdevai/test_detr_finetuned_cppe5/checkpoint-321/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/bpdevai/test_detr_finetuned_cppe5/resolve/main/checkpoint-321/optimizer.pt
346 MB
- Xet hash:
- 4fb74bfac68a30e9e1699410079e99a7c54a0223362ab1155da3243cbb92de68
- Size of remote file:
- 346 MB
- SHA256:
- 1c225e7161717287996b39e83c403a512ab161420c33cd2cabc2c0e9eb3eaa12
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.