Instructions to use binitt/detr-resnet-50_finetuned_cppe5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binitt/detr-resnet-50_finetuned_cppe5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="binitt/detr-resnet-50_finetuned_cppe5")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("binitt/detr-resnet-50_finetuned_cppe5") model = AutoModelForObjectDetection.from_pretrained("binitt/detr-resnet-50_finetuned_cppe5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from binitt/detr-resnet-50_finetuned_cppe5: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/binitt/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
- Command line
-
hf download hf://binitt/detr-resnet-50_finetuned_cppe5/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/binitt/detr-resnet-50_finetuned_cppe5/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 623bbd2efe86ece8e274da2215a2aa5f5a5ba4e65033bb28810d43a334036bc1
- Size of remote file:
- 4.73 kB
- SHA256:
- b2182e41824d6f4b9304ef2e6845e98f7d6bc4224f9040309539fb9d8cde17f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.