Instructions to use facebook/detr-resnet-101-dc5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/detr-resnet-101-dc5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="facebook/detr-resnet-101-dc5")# Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-101-dc5") model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-101-dc5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/detr-resnet-101-dc5: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/facebook/detr-resnet-101-dc5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/detr-resnet-101-dc5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/detr-resnet-101-dc5/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 9bdad82950da79024542f9249987f38645f99c902155efd780f78ab86a69b2ac
- Size of remote file:
- 243 MB
- SHA256:
- 7bd3d719aaea1dc63844e7ced0c820fa5e4cc7b041beea54f18559a88717f287
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