Instructions to use hsanchez/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hsanchez/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="hsanchez/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("hsanchez/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("hsanchez/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hsanchez/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 167 MB
-
https://huggingface.co/hsanchez/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hsanchez/detr-resnet-50_finetuned_cppe5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hsanchez/detr-resnet-50_finetuned_cppe5/resolve/main/pytorch_model.bin
167 MB
- Xet hash:
- 4002c014ccf8291cab2720e3be3a2af8cbb645c9ed9c6ce9024db49d5245b1a9
- Size of remote file:
- 167 MB
- SHA256:
- 191e650c96d0faacdcf7af8489ed8f5edf9d640381cf65333e94126ecbb1e0f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.