Instructions to use facebook/detr-resnet-50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/detr-resnet-50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="facebook/detr-resnet-50")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50") model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/detr-resnet-50: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/facebook/detr-resnet-50/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/detr-resnet-50/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/detr-resnet-50/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- db2d2df7baa2a685614043a33ed06c95f260913709e924340482a1c537e78f37
- Size of remote file:
- 167 MB
- SHA256:
- 9400d5a6a433c73bb3440f42daab69a7b728b4bce0922904ac4779cb04e08989
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