Instructions to use acbdkk/cifar10model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acbdkk/cifar10model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="acbdkk/cifar10model") 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("acbdkk/cifar10model") model = AutoModelForImageClassification.from_pretrained("acbdkk/cifar10model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update config.json
Browse files- config.json +1 -1
config.json
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"architectures": ["
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"hidden_size": 768,
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"num_hidden_layers": 12,
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"num_attention_heads": 12,
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{
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"architectures": ["ViTForImageClassification"],
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"hidden_size": 768,
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"num_hidden_layers": 12,
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"num_attention_heads": 12,
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