Instructions to use jays009/Restnet50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jays009/Restnet50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jays009/Restnet50", device_map="auto") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jays009/Restnet50", dtype="auto", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- config.json +1 -0
- pytorch_model.bin +3 -0
config.json
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{"architectures": ["ResNet"], "num_labels": 2, "input_size": 224, "image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225]}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:bf3fcb2885dcd8c9f19df562bf751f5506747ebbe2d134f4a704a6dcd450e5f0
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size 94363594
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