Instructions to use cvnberk/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cvnberk/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="cvnberk/detr-resnet-50_finetuned_cppe5")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("cvnberk/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("cvnberk/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from cvnberk/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/cvnberk/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://cvnberk/detr-resnet-50_finetuned_cppe5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/cvnberk/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- fb95910bdc8d73ed52ca90f12de7b0504391dac37826b912c13d728efc989db3
- Size of remote file:
- 167 MB
- SHA256:
- 910e83c8e587f8ef8a7cb2c482d07eb273224ef0b2eac532d4a353a26525c6b1
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