Instructions to use ThongCoder/microsoft-resnet50-cifar100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThongCoder/microsoft-resnet50-cifar100 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ThongCoder/microsoft-resnet50-cifar100") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ThongCoder/microsoft-resnet50-cifar100") model = AutoModelForImageClassification.from_pretrained("ThongCoder/microsoft-resnet50-cifar100", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 37ce677a46aec04f445c4bc75b5ebd13abb1e2c917e7452a35cab4f8da6f6db9
- Size of remote file:
- 5.78 kB
- SHA256:
- ed728c0eb0b419708c4389cbe77ac9b2ee5feb7754668c6315e73ee0e8e5b6eb
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