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Add the ViTamin model, which is trained on public DataComp-1B using OpenCLIP framework and obtains 82.9% zero-shot ImageNet-1K accuracy with 436M parameters. It achieves the state-of-the-art performance on zero-shot image classification, multi-modal retrieval, open-vocabulary detection and segmentation, and large multi-model models.
The code of ViTamin models are modified from vision_transformer_hybrid.py in the timm codebase.
This ViTamin work has been accepted to CVPR 2024 (https://arxiv.org/pdf/2404.02132).