Transformer-based Vision Model

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A Transformer-based Vision Model is a vision model that is a transformer-based model.



References

2023

  • GBard
    • A Vision Transformer-based Model is a deep learning model that is inspired by the Transformer architecture, which was originally designed for natural language processing (NLP) tasks.
    • Vision Transformers (ViTs) represent images as a sequence of patches, just like NLP models represent text as a sequence of words. This allows ViTs to learn long-range dependencies between different parts of an image, which is essential for many computer vision tasks, such as image classification and object detection.
    • ViTs have achieved state-of-the-art results on a variety of computer vision benchmarks, and they are quickly becoming the preferred model architecture for many tasks.
    • Here are some examples of Vision Transformer-based Models:
      • DeiT (Data-efficient Image Transformer)
      • ViLT (Vision and Language Transformer)
      • Swin Transformer
      • ConvMixer
      • Pyramid Vision Transformer