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VisionLLaMA is a new vision transformer architecture that adapts the successful LLaMA language model design for vision tasks. By integrating components like rotary positional embeddings, SwiGLU activation, and LayerNorm from LLaMA, VisionLLaMA achieves very promising performance across various vision tasks, including image generation, classification, semantic segmentation, and object detection.
VisionLLaMA is a new vision transformer architecture that adapts the successful LLaMA language model design for vision tasks. By integrating components like rotary positional embeddings, SwiGLU activation, and LayerNorm from LLaMA, VisionLLaMA achieves very promising performance across various vision tasks, including image generation, classification, semantic segmentation, and object detection.
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Keypoints:
Keypoints:
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* Outperforms state-of-the-art vision transformers like DiT, SiT, DeiT3, and Swin on multiple benchmarks and tasks.
* Outperforms state-of-the-art vision transformers like DiT, SiT, DeiT3, and Swin on multiple benchmarks and tasks.
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* Leverages Auto-Scaled 2D Rotary Positional Embeddings (AS2DRoPE) to handle variable input resolutions efficiently.
* Leverages Auto-Scaled 2D Rotary Positional Embeddings (AS2DRoPE) to handle variable input resolutions efficiently.
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* Serves as a powerful, unified modeling framework for vision generation and understanding tasks.
* Serves as a powerful, unified modeling framework for vision generation and understanding tasks.
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Paper: 
Paper: 
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https://huggingface.co/papers/2403.00522
paper
2403.00522
https://huggingface.co/papers/2403.00522
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks (2403.00522)
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GitHub repo: 
GitHub repo: 
link
https://github.com/Meituan-AutoML/VisionLLaMA
https://github.com/Meituan-AutoML/VisionLLaMA
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Congrats to the authors for their work!
Congrats to the authors for their work!
VisionLLaMA is a new vision transformer architecture that adapts the successful LLaMA language model design for vision tasks. By integrating components like rotary positional embeddings, SwiGLU activation, and LayerNorm from LLaMA, VisionLLaMA achieves very promising performance across various vision tasks, including image generation, classification, semantic segmentation, and object detection.

Keypoints:
* Outperforms state-of-the-art vision transformers like DiT, SiT, DeiT3, and Swin on multiple benchmarks and tasks.
* Leverages Auto-Scaled 2D Rotary Positional Embeddings (AS2DRoPE) to handle variable input resolutions efficiently.
* Serves as a powerful, unified modeling framework for vision generation and understanding tasks.

Paper: https://huggingface.co/papers/2403.00522
GitHub repo: https://github.com/Meituan-AutoML/VisionLLaMA

Congrats to the authors for their work!
❤️
clem jucamohedano osanseviero victor samusenps sgarbi MexIvanov trysem