Instructions to use amaye15/aimv2-large-patch14-native-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amaye15/aimv2-large-patch14-native-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="amaye15/aimv2-large-patch14-native-image-classification", trust_remote_code=True) pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForImageClassification processor = AutoProcessor.from_pretrained("amaye15/aimv2-large-patch14-native-image-classification", trust_remote_code=True) model = AutoModelForImageClassification.from_pretrained("amaye15/aimv2-large-patch14-native-image-classification", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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- **Original Model**: `apple/aimv2-large-patch14-native`
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- **Adaptation**: Modified to be compatible with `AutoModelForImageClassification` for direct use in image classification tasks.
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- **Framework**: PyTorch
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- **License**: [Specify license if applicable]
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## Citation
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- **Original Model**: `apple/aimv2-large-patch14-native`
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- **Adaptation**: Modified to be compatible with `AutoModelForImageClassification` for direct use in image classification tasks.
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- **Framework**: PyTorch
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## Citation
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