MambaOut: Do We Really Need Mamba for Vision? - GitHub
WEB"What can I say, Mamba out." — Kobe Bryant, NBA farewell speech, 2016. Image credit: https://www.ebay.ca/itm/264973452480. This is a PyTorch implementation of MambaOut proposed by our paper "MambaOut: Do We Really Need Mamba for Vision?".
GitHub - state-spaces/mamba: Mamba SSM architecture
WEBMamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers.
[2405.07992] MambaOut: Do We Really Need Mamba for Vision?
WEBMay 13, 2024 · To empirically verify our hypotheses, we construct a series of models named MambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses.
WEBMambaOut through stacking Mamba blocks while removing their core token mixer, SSM. Experimental results strongly support our hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models on ImageNet image classification, indicating that Mamba is indeed unnecessary for this task. As
Paper page - MambaOut: Do We Really Need Mamba for Vision?
WEBAs for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks. The code is available at https://github.com/yuweihao/MambaOut
MambaOut: Do We Really Need Mamba for Vision? - Papers With …
WEBMay 13, 2024 · As for detection and segmentation, MambaOut cannot match the performance of state-of-the-art visual Mamba models, demonstrating the potential of Mamba for long-sequence visual tasks. The code is available at https://github.com/yuweihao/MambaOut
Releases · yuweihao/MambaOut - GitHub
WEBMambaOut: Do We Really Need Mamba for Vision? Contribute to yuweihao/MambaOut development by creating an account on GitHub.
codestral_mamba.md · GitHub
WEBMamba-Codestral is a 7B parameter model that combines the strengths of Mamba (a state space model) with Mistral's Mixture of Experts (MoE) architecture. Key features include: Efficient training and inference. Strong performance on code-related tasks. Competitive with larger models on general language tasks.
MambaOut: Do We Really Need Mamba for Vision? - arXiv.org
WEBMay 14, 2024 · Experiments show that MambaOut effectively surpasses visual Mamba models in ImageNet image classification but does not reach the performance of state-of-the-art visual Mamba models in detection and segmentation tasks.
MambaOut: Do We Really Need Mamba for Vision?
WEBMay 20, 2024 · Mamba is a novel architecture that uses a recurrent neural network (RNN)-like token mixer to address the quadratic complexity of the attention mechanism, which has been applied to vision tasks. However, Mamba's performance in vision tasks has often been underwhelming compared to convolutional and attention-based models.
- Some results have been removed
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