Instructions to use DeepLearner101/CIFAR100SelectedSubsetForTrainingBasedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepLearner101/CIFAR100SelectedSubsetForTrainingBasedModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DeepLearner101/CIFAR100SelectedSubsetForTrainingBasedModel") 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("DeepLearner101/CIFAR100SelectedSubsetForTrainingBasedModel") model = AutoModelForImageClassification.from_pretrained("DeepLearner101/CIFAR100SelectedSubsetForTrainingBasedModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99142dcb7c78a9dfe1e65d6a75d044832c33066d55b6ebb9e7213982c57cea34
- Size of remote file:
- 94.4 MB
- SHA256:
- aad2ad8e0261c160ec1e4a156ff65a1ae97dc2b18aaac9e6c032a5db7a92bf2e
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