Instructions to use peter881122/freeway_resnet50_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peter881122/freeway_resnet50_Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="peter881122/freeway_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/freeway_resnet50_Model") model = AutoModelForImageClassification.from_pretrained("peter881122/freeway_resnet50_Model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from peter881122/freeway_resnet50_Model: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/peter881122/freeway_resnet50_Model/resolve/main/training_args.bin
- Command line
-
hf download hf://peter881122/freeway_resnet50_Model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/peter881122/freeway_resnet50_Model/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- c7ab37941fac0eb4ba2c1aea70fe626442a37483e2bd74da382103b53de218b8
- Size of remote file:
- 5.11 kB
- SHA256:
- 3b378f23049ba93a6c1e8961e81f668b55ed3f801f1c0cda3744c83d50b14134
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