Instructions to use jony4583/ddetr-cvpdl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jony4583/ddetr-cvpdl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="jony4583/ddetr-cvpdl")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("jony4583/ddetr-cvpdl") model = AutoModelForObjectDetection.from_pretrained("jony4583/ddetr-cvpdl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from jony4583/ddetr-cvpdl: direct link, hf CLI and curl.
- Browser
- Download file 165 MB
-
https://huggingface.co/jony4583/ddetr-cvpdl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://jony4583/ddetr-cvpdl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/jony4583/ddetr-cvpdl/resolve/main/pytorch_model.bin
165 MB
- Xet hash:
- 10252320389d2e3e5d671bb6a42df44745c11c1b8eced29b8a7227e0f2daa94a
- Size of remote file:
- 165 MB
- SHA256:
- 226fb6fc989cebc6f907434bc65cb743cdfbe547182fa9213878f02f76fc9911
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