Instructions to use cd-daniel/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cd-daniel/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="cd-daniel/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("cd-daniel/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("cd-daniel/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from cd-daniel/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 4.03 kB
-
https://huggingface.co/cd-daniel/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://cd-daniel/detr-resnet-50_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/cd-daniel/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
4.03 kB
- Xet hash:
- e0c43244f965a25bdc7366ead3e6b7f891ffa46c163ed9b2bbd7fbcc7ce0c244
- Size of remote file:
- 4.03 kB
- SHA256:
- f23b1c8613c1724e2fbe10a16d53e697976b40bf340d60d355b7ad7458c5d37f
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