Instructions to use memogamd/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use memogamd/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="memogamd/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("memogamd/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("memogamd/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from memogamd/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 3.64 kB
-
https://huggingface.co/memogamd/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://memogamd/detr-resnet-50_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/memogamd/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
3.64 kB
- Xet hash:
- 98e7fc194a06d2bbaf667dd9f63d87928fac09233e45b2b8981a795995e42076
- Size of remote file:
- 3.64 kB
- SHA256:
- 24e2794c304893210b7a97929687e433c9f20813085e712bc659150f9e0d1fbc
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