Papers › EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
High-resolution dense prediction enables many appealing real-world applications, such as computational photography, autonomous driving, etc. However, the vast computational cost makes deploying state-of-the-art high-resolution dense prediction models on hardware devices difficult. This work presents EfficientViT, a new family of high-resolution vision models with novel multi-scale linear attention. Unlike prior high-resolution dense prediction models that rely on heavy softmax attention, hardware-inefficient large-kernel convolution, or complicated topology structure to obtain good performances, our multi-scale linear attention achieves the global receptive field and multi-scale learning (two desirable features for high-resolution dense prediction) with only lightweight and hardware-efficient operations. As such, EfficientViT delivers remarkable performance gains over previous state-of-the-art models with significant speedup on diverse hardware platforms, including mobile CPU, edge GPU, and cloud GPU. Without performance loss on Cityscapes, our EfficientViT provides up to 13.9× and 6.2× GPU latency reduction over SegFormer and SegNeXt, respectively. For super-resolution, EfficientViT delivers up to 6.4x speedup over Restormer while providing 0.11dB gain in PSNR. For Segment Anything, EfficientViT delivers 48.9x higher throughput on A100 GPU while achieving slightly better zero-shot instance segmentation performance on COCO.
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Tasks
2 archive task tags without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | EfficientViT-L2 (r384) | GFLOPs | 20 | #181 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L2 (r384) | Number of params | 64M | #181 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L2 (r384) | Top 1 Accuracy | 86% | #181 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L2 (r288) | GFLOPs | 11 | #215 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L2 (r288) | Number of params | 64M | #215 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L2 (r288) | Top 1 Accuracy | 85.6% | #215 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L1 (r224) | GFLOPs | 5.3 | #310 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L1 (r224) | Number of params | 53M | #310 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-L1 (r224) | Top 1 Accuracy | 84.5% | #310 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B3 (r288) | GFLOPs | 6.5 | #337 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B3 (r288) | Number of params | 49M | #337 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B3 (r288) | Top 1 Accuracy | 84.2% | #337 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B3 (r224) | GFLOPs | 4 | #420 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B3 (r224) | Top 1 Accuracy | 83.5% | #420 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B2 (r256) | GFLOPs | 2.1 | #511 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B2 (r256) | Number of params | 24M | #511 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | EfficientViT-B2 (r256) | Top 1 Accuracy | 82.7% | #511 of 1060 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | EfficientViT-B3 (r512) | Validation mIoU | 49 | #142 of 235 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | EfficientViT-B3 (r1184x2368) | mIoU | 83.2 | #27 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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