Instructions to use peter881122/road_resnet50_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter881122/road_resnet50_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="peter881122/road_resnet50_Model") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("peter881122/road_resnet50_Model") model = AutoModelForImageClassification.from_pretrained("peter881122/road_resnet50_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from peter881122/road_resnet50_Model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/peter881122/road_resnet50_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://peter881122/road_resnet50_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/peter881122/road_resnet50_Model/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- e1e2f1a3b6a4cc8f6f4a032ead8663ab1908f5b6961f096c29b0c30ba85f224b
- Size of remote file:
- 5.11 kB
- SHA256:
- 101ece8e71150ec0b11fdf9567219cd3a012152994ffa08f01b9aedeeabf97e7
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