Instructions to use facebook/detr-resnet-101 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/detr-resnet-101 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="facebook/detr-resnet-101")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-101") model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-101", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/detr-resnet-101: direct link, hf CLI and curl.
- Browser
- Download file 243 MB
-
https://huggingface.co/facebook/detr-resnet-101/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/detr-resnet-101/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/detr-resnet-101/resolve/main/pytorch_model.bin
243 MB
- Xet hash:
- 7c5b329dc5dc317d018b33f81e3145b4d65f8e5bf3fb63f08cc85880a2650d7b
- Size of remote file:
- 243 MB
- SHA256:
- 0943b5a9085a95a0e3ecc1c99a7db0451ecb9d79f4dcb543b0939c1a12481a5d
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