[ICLR 2024] MogaNet: Efficient Multi-order Gated Aggregation Network
Siyuan Li*,1,2, Zedong Wang*,1, Zicheng Liu1,2, Chen Tan1,2, Haitao Lin1,2, Di Wu1,2, Zhiyuan Chen1, Jiangbin Zheng1,2, Stan Z. Li†,1
We propose MogaNet, a new family of efficient ConvNets designed through the lens of multi-order game-theoretic interaction, to pursue informative context mining with preferable complexity-performance trade-offs. It shows excellent scalability and attains competitive results among state-of-the-art models with more efficient use of model parameters on ImageNet and multifarious typical vision benchmarks, including COCO object detection, ADE20K semantic segmentation, 2D&3D human pose estimation, and video prediction.
This repository contains PyTorch implementation for MogaNet (ICLR 2024).
We plan to release implementations of MogaNet in a few months. Please watch us for the latest release. Currently, this repo is reimplemented according to our official implementations in OpenMixup, and we are working on cleaning up experimental results and code implementations. Models are released in GitHub / Baidu Cloud / Hugging Face.
Please check INSTALL.md for installation instructions.
See TRAINING.md for ImageNet-1K training and validation instructions, or refer to our OpenMixup implementations. We released pre-trained models on OpenMixup in moganet-in1k-weights. We have also reproduced ImageNet results with this repo and released args.yaml
/ summary.csv
/ model.pth.tar
in moganet-in1k-weights. The parameters in the trained model can be extracted by code.
Here is a notebook demo of MogaNet which run the steps to perform inference with MogaNet for image classification.
Model | Resolution | Params (M) | Flops (G) | Top-1 / top-5 (%) | Script | Download |
---|---|---|---|---|---|---|
MogaNet-XT | 224x224 | 2.97 | 0.80 | 76.5 | 93.4 | args | script | model | log |
MogaNet-XT | 256x256 | 2.97 | 1.04 | 77.2 | 93.8 | args | script | model | log |
MogaNet-T | 224x224 | 5.20 | 1.10 | 79.0 | 94.6 | args | script | model | log |
MogaNet-T | 256x256 | 5.20 | 1.44 | 79.6 | 94.9 | args | script | model | log |
MogaNet-T* | 256x256 | 5.20 | 1.44 | 80.0 | 95.0 | config | script | model | log |
MogaNet-S | 224x224 | 25.3 | 4.97 | 83.4 | 96.9 | args | script | model | log |
MogaNet-B | 224x224 | 43.9 | 9.93 | 84.3 | 97.0 | args | script | model | log |
MogaNet-L | 224x224 | 82.5 | 15.9 | 84.7 | 97.1 | args | script | model | log |
MogaNet-XL | 224x224 | 180.8 | 34.5 | 85.1 | 97.4 | args | script | model | log |
(1) The code to count MACs of MogaNet variants.
python get_flops.py --model moganet_tiny
(2) The code to visualize Grad-CAM activation maps (or variants of Grad-CAM) of MogaNet and other popular architectures.
python cam_image.py --use_cuda --image_path /path/to/image.JPEG --model moganet_tiny --method gradcam
Method | Backbone | Pretrain | Params | FLOPs | Lr schd | box mAP | mask mAP | Config | Download |
---|---|---|---|---|---|---|---|---|---|
Mask R-CNN | MogaNet-XT | ImageNet-1K | 22.8M | 185.4G | 1x | 40.7 | 37.6 | config | log / model |
Mask R-CNN | MogaNet-T | ImageNet-1K | 25.0M | 191.7G | 1x | 42.6 | 39.1 | config | log / model |
Mask R-CNN | MogaNet-S | ImageNet-1K | 45.0M | 271.6G | 1x | 46.6 | 42.2 | config | log / model |
Mask R-CNN | MogaNet-B | ImageNet-1K | 63.4M | 373.1G | 1x | 49.0 | 43.8 | config | log / model |
Mask R-CNN | MogaNet-L | ImageNet-1K | 102.1M | 495.3G | 1x | 49.4 | 44.2 | config | log / model |
Mask R-CNN | MogaNet-T | ImageNet-1K | 25.0M | 191.7G | MS 3x | 45.3 | 40.7 | config | log / model |
Mask R-CNN | MogaNet-S | ImageNet-1K | 45.0M | 271.6G | MS 3x | 48.5 | 43.1 | config | log / model |
Mask R-CNN | MogaNet-B | ImageNet-1K | 63.4M | 373.1G | MS 3x | 50.3 | 44.4 | config | log / model |
Mask R-CNN | MogaNet-L | ImageNet-1K | 63.4M | 373.1G | MS 3x | 50.6 | 44.6 | config | log / model |
Method | Backbone | Pretrain | Params | FLOPs | Lr schd | box mAP | Config | Download |
---|---|---|---|---|---|---|---|---|
RetinaNet | MogaNet-XT | ImageNet-1K | 12.1M | 167.2G | 1x | 39.7 | config | log / model |
RetinaNet | MogaNet-T | ImageNet-1K | 14.4M | 173.4G | 1x | 41.4 | config | log / model |
RetinaNet | MogaNet-S | ImageNet-1K | 35.1M | 253.0G | 1x | 45.8 | config | log / model |
RetinaNet | MogaNet-B | ImageNet-1K | 53.5M | 354.5G | 1x | 47.7 | config | log / model |
RetinaNet | MogaNet-L | ImageNet-1K | 92.4M | 476.8G | 1x | 48.7 | config | log / model |
Method | Backbone | Pretrain | Params | FLOPs | Lr schd | box mAP | mask mAP | Config | Download |
---|---|---|---|---|---|---|---|---|---|
Cascade Mask R-CNN | MogaNet-S | ImageNet-1K | 77.9M | 405.4G | MS 3x | 51.4 | 44.9 | config | log / model |
Cascade Mask R-CNN | MogaNet-S | ImageNet-1K | 82.8M | 750.2G | GIOU+MS 3x | 51.7 | 45.1 | config | log / model |
Cascade Mask R-CNN | MogaNet-B | ImageNet-1K | 101.2M | 851.6G | GIOU+MS 3x | 52.6 | 46.0 | config | log / model |
Cascade Mask R-CNN | MogaNet-L | ImageNet-1K | 139.9M | 973.8G | GIOU+MS 3x | 53.3 | 46.1 | config | - |
Method | Backbone | Pretrain | Params | FLOPs | Iters | mIoU | mAcc | Config | Download |
---|---|---|---|---|---|---|---|---|---|
Semantic FPN | MogaNet-XT | ImageNet-1K | 6.9M | 101.4G | 80K | 40.3 | 52.4 | config | log / model |
Semantic FPN | MogaNet-T | ImageNet-1K | 9.1M | 107.8G | 80K | 43.1 | 55.4 | config | log / model |
Semantic FPN | MogaNet-S | ImageNet-1K | 29.1M | 189.7G | 80K | 47.7 | 59.8 | config | log / model |
Semantic FPN | MogaNet-B | ImageNet-1K | 47.5M | 293.6G | 80K | 49.3 | 61.6 | config | log / model |
Semantic FPN | MogaNet-L | ImageNet-1K | 86.2M | 418.7G | 80K | 50.2 | 63.0 | config | log / model |
Method | Backbone | Pretrain | Params | FLOPs | Iters | mIoU | mAcc | Config | Download |
---|---|---|---|---|---|---|---|---|---|
UperNet | MogaNet-XT | ImageNet-1K | 30.4M | 855.7G | 160K | 42.2 | 55.1 | config | log / model |
UperNet | MogaNet-T | ImageNet-1K | 33.1M | 862.4G | 160K | 43.7 | 57.1 | config | log / model |
UperNet | MogaNet-S | ImageNet-1K | 55.3M | 946.4G | 160K | 49.2 | 61.6 | config | log / model |
UperNet | MogaNet-B | ImageNet-1K | 73.7M | 1050.4G | 160K | 50.1 | 63.4 | config | log / model |
UperNet | MogaNet-L | ImageNet-1K | 113.2M | 1176.1G | 160K | 50.9 | 63.5 | config | log / model |
Backbone | Input Size | Params | FLOPs | AP | AP50 | AP75 | AR | ARM | ARL | Config | Download |
---|---|---|---|---|---|---|---|---|---|---|---|
MogaNet-XT | 256x192 | 5.6M | 1.8G | 72.1 | 89.7 | 80.1 | 77.7 | 73.6 | 83.6 | config | log | model |
MogaNet-XT | 384x288 | 5.6M | 4.2G | 74.7 | 90.1 | 81.3 | 79.9 | 75.9 | 85.9 | config | log | model |
MogaNet-T | 256x192 | 8.1M | 2.2G | 73.2 | 90.1 | 81.0 | 78.8 | 74.9 | 84.4 | config | log | model |
MogaNet-T | 384x288 | 8.1M | 4.9G | 75.7 | 90.6 | 82.6 | 80.9 | 76.8 | 86.7 | config | log | model |
MogaNet-S | 256x192 | 29.0M | 6.0G | 74.9 | 90.7 | 82.8 | 80.1 | 75.7 | 86.3 | config | log | model |
MogaNet-S | 384x288 | 29.0M | 13.5G | 76.4 | 91.0 | 83.3 | 81.4 | 77.1 | 87.7 | config | log | model |
MogaNet-B | 256x192 | 47.4M | 10.9G | 75.3 | 90.9 | 83.3 | 80.7 | 76.4 | 87.1 | config | log | model |
MogaNet-B | 384x288 | 47.4M | 24.4G | 77.3 | 91.4 | 84.0 | 82.2 | 77.9 | 88.5 | config | log | model |
Architecture | Setting | Params | FLOPs | FPS | MSE | MAE | SSIM | PSNR | Download |
---|---|---|---|---|---|---|---|---|---|
IncepU (SimVPv1) | 200 epoch | 58.0M | 19.4G | 209 | 32.15 | 89.05 | 0.9268 | 21.84 | model | log |
gSTA (SimVPv2) | 200 epoch | 46.8M | 16.5G | 282 | 26.69 | 77.19 | 0.9402 | 22.78 | model | log |
ViT | 200 epoch | 46.1M | 16.9G | 290 | 35.15 | 95.87 | 0.9139 | 21.67 | model | log |
Swin Transformer | 200 epoch | 46.1M | 16.4G | 294 | 29.70 | 84.05 | 0.9331 | 22.22 | model | log |
Uniformer | 200 epoch | 44.8M | 16.5G | 296 | 30.38 | 85.87 | 0.9308 | 22.13 | model | log |
MLP-Mixer | 200 epoch | 38.2M | 14.7G | 334 | 29.52 | 83.36 | 0.9338 | 22.22 | model | log |
ConvMixer | 200 epoch | 3.9M | 5.5G | 658 | 32.09 | 88.93 | 0.9259 | 21.93 | model | log |
Poolformer | 200 epoch | 37.1M | 14.1G | 341 | 31.79 | 88.48 | 0.9271 | 22.03 | model | log |
ConvNeXt | 200 epoch | 37.3M | 14.1G | 344 | 26.94 | 77.23 | 0.9397 | 22.74 | model | log |
VAN | 200 epoch | 44.5M | 16.0G | 288 | 26.10 | 76.11 | 0.9417 | 22.89 | model | log |
HorNet | 200 epoch | 45.7M | 16.3G | 287 | 29.64 | 83.26 | 0.9331 | 22.26 | model | log |
MogaNet | 200 epoch | 46.8M | 16.5G | 255 | 25.57 | 75.19 | 0.9429 | 22.99 | model | log |
IncepU (SimVPv1) | 2000 epoch | 58.0M | 19.4G | 209 | 21.15 | 64.15 | 0.9536 | 23.99 | model | log |
gSTA (SimVPv2) | 2000 epoch | 46.8M | 16.5G | 282 | 15.05 | 49.80 | 0.9675 | 25.97 | model | log |
ViT | 2000 epoch | 46.1M | 16.9.G | 290 | 19.74 | 61.65 | 0.9539 | 24.59 | model | log |
Swin Transformer | 2000 epoch | 46.1M | 16.4G | 294 | 19.11 | 59.84 | 0.9584 | 24.53 | model | log |
Uniformer | 2000 epoch | 44.8M | 16.5G | 296 | 18.01 | 57.52 | 0.9609 | 24.92 | model | log |
MLP-Mixer | 2000 epoch | 38.2M | 14.7G | 334 | 18.85 | 59.86 | 0.9589 | 24.58 | model | log |
ConvMixer | 2000 epoch | 3.9M | 5.5G | 658 | 22.30 | 67.37 | 0.9507 | 23.73 | model | log |
Poolformer | 2000 epoch | 37.1M | 14.1G | 341 | 20.96 | 64.31 | 0.9539 | 24.15 | model | log |
ConvNeXt | 2000 epoch | 37.3M | 14.1G | 344 | 17.58 | 55.76 | 0.9617 | 25.06 | model | log |
VAN | 2000 epoch | 44.5M | 16.0G | 288 | 16.21 | 53.57 | 0.9646 | 25.49 | model | log |
HorNet | 2000 epoch | 45.7M | 16.3G | 287 | 17.40 | 55.70 | 0.9624 | 25.14 | model | log |
MogaNet | 2000 epoch | 46.8M | 16.5G | 255 | 15.67 | 51.84 | 0.9661 | 25.70 | model | log |
Architecture | Setting | Params | FLOPs | FPS | MSE | MAE | SSIM | PSNR | Download |
---|---|---|---|---|---|---|---|---|---|
IncepU (SimVPv1) | 200 epoch | 58.0M | 19.4G | 209 | 30.77 | 113.94 | 0.8740 | 21.81 | model | log |
gSTA (SimVPv2) | 200 epoch | 46.8M | 16.5G | 282 | 25.86 | 101.22 | 0.8933 | 22.61 | model | log |
ViT | 200 epoch | 46.1M | 16.9.G | 290 | 31.05 | 115.59 | 0.8712 | 21.83 | model | log |
Swin Transformer | 200 epoch | 46.1M | 16.4G | 294 | 28.66 | 108.93 | 0.8815 | 22.08 | model | log |
Uniformer | 200 epoch | 44.8M | 16.5G | 296 | 29.56 | 111.72 | 0.8779 | 21.97 | model | log |
MLP-Mixer | 200 epoch | 38.2M | 14.7G | 334 | 28.83 | 109.51 | 0.8803 | 22.01 | model | log |
ConvMixer | 200 epoch | 3.9M | 5.5G | 658 | 31.21 | 115.74 | 0.8709 | 21.71 | model | log |
Poolformer | 200 epoch | 37.1M | 14.1G | 341 | 30.02 | 113.07 | 0.8750 | 21.95 | model | log |
ConvNeXt | 200 epoch | 37.3M | 14.1G | 344 | 26.41 | 102.56 | 0.8908 | 22.49 | model | log |
VAN | 200 epoch | 44.5M | 16.0G | 288 | 31.39 | 116.28 | 0.8703 | 22.82 | model | log |
HorNet | 200 epoch | 45.7M | 16.3G | 287 | 29.19 | 110.17 | 0.8796 | 22.03 | model | log |
MogaNet | 200 epoch | 46.8M | 16.5G | 255 | 25.14 | 99.69 | 0.8960 | 22.73 | model | log |
This project is released under the Apache 2.0 license.
Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.
If you find this repository helpful, please consider citing:
@inproceedings{iclr2024MogaNet,
title={MogaNet: Multi-order Gated Aggregation Network},
author={Siyuan Li and Zedong Wang and Zicheng Liu and Cheng Tan and Haitao Lin and Di Wu and Zhiyuan Chen and Jiangbin Zheng and Stan Z. Li},
booktitle={International Conference on Learning Representations},
year={2024}
}