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-214/scheduler.pt from bpdevai/test_detr_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/bpdevai/test_detr_finetuned_cppe5/resolve/main/checkpoint-214/scheduler.pt
- Command line
-
hf download hf://bpdevai/test_detr_finetuned_cppe5/checkpoint-214/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/bpdevai/test_detr_finetuned_cppe5/resolve/main/checkpoint-214/scheduler.pt
1.06 kB
- Xet hash:
- d4c86222a78b5ddecff908a3bbadd6ec09fb6ef474869ea8525e1dc0b53e0bd1
- Size of remote file:
- 1.06 kB
- SHA256:
- 885dee9a1fb4a13398120e6b2bbe57669ebf3324425a7bc32fbe9e50fc47d893
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.