--- license: apache-2.0 --- **[CVPR'25] Official PyTorch implementation of "[**MobileMamba: Lightweight Multi-Receptive Visual Mamba Network**](https://arxiv.org/pdf/2411.15941)".** --- [![arXiv](https://img.shields.io/badge/Arxiv-2411.15941-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2411.15941) [![github](https://img.shields.io/github/stars/lewandofskee/MobileMamba.svg?style=social)](https://github.com/lewandofskee/MobileMamba) [Haoyang He1*](https://scholar.google.com/citations?hl=zh-CN&user=8NfQv1sAAAAJ), [Jiangning Zhang2*](https://zhangzjn.github.io), [Yuxuan Cai3](https://scholar.google.com/citations?hl=zh-CN&user=J9lTFAUAAAAJ), [Hongxu Chen1](https://scholar.google.com/citations?hl=zh-CN&user=uFT3YfMAAAAJ) [Xiaobin Hu2](https://scholar.google.com/citations?hl=zh-CN&user=3lMuodUAAAAJ), [Zhenye Gan2](https://scholar.google.com/citations?user=fa4NkScAAAAJ&hl=zh-CN), [Yabiao Wang2](https://scholar.google.com/citations?user=xiK4nFUAAAAJ&hl=zh-CN), [Chengjie Wang2](https://scholar.google.com/citations?hl=zh-CN&user=fqte5H4AAAAJ), Yunsheng Wu2, [Lei Xie1†](https://scholar.google.com/citations?hl=zh-CN&user=7ZZ_-m0AAAAJ) 1College of Control Science and Engineering, Zhejiang University, 2Youtu Lab, Tencent, 3Huazhong University of Science and Technology > **Abstract:** Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios. Recently, state-space models have gained popularity in the visual domain due to their linear computational complexity. Despite their low FLOPs, current lightweight Mamba-based models exhibit suboptimal throughput. In this work, we propose the MobileMamba framework, which balances efficiency and performance. We design a three-stage network to enhance inference speed significantly. At a fine-grained level, we introduce the Multi-Receptive Field Feature Interaction MRFFI module, comprising the Long-Range Wavelet Transform-Enhanced Mamba WTE-Mamba, Efficient Multi-Kernel Depthwise Convolution MK-DeConv, and Eliminate Redundant Identity components. This module integrates multi-receptive field information and enhances high-frequency detail extraction. Additionally, we employ training and testing strategies to further improve performance and efficiency. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods which is maximum x21 faster than LocalVim on GPU. Extensive experiments on high-resolution downstream tasks demonstrate that MobileMamba surpasses current efficient models, achieving an optimal balance between speed and accuracy. ------ # Classification results ### Image Classification for [ImageNet-1K](https://www.image-net.org): | Model | FLOPs | #Params | Resolution | Top-1 | Cfg | Log | Model | |--------------------------|:-----:|:-------:|:----------:|:-----:|:---------------------------------------------:|:--------------------------------------------------:|:----------------------------------------------------:| | MobileMamba-T2 | 255M | 8.8M | 192 x 192 | 71.5 | [cfg](configs/mobilemamba/mobilemamba_t2.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T2/mobilemamba_t2.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T2/mobilemamba_t2.pth) | | MobileMamba-T2† | 255M | 8.8M | 192 x 192 | 76.9 | [cfg](configs/mobilemamba/mobilemamba_t2s.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T2s/mobilemamba_t2s.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T2s/mobilemamba_t2s.pth) | | MobileMamba-T4 | 413M | 14.2M | 192 x 192 | 76.1 | [cfg](configs/mobilemamba/mobilemamba_t4.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T4/mobilemamba_t4.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T4/mobilemamba_t4.pth) | | MobileMamba-T4† | 413M | 14.2M | 192 x 192 | 78.9 | [cfg](configs/mobilemamba/mobilemamba_t4s.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T4s/mobilemamba_t4s.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_T4s/mobilemamba_t4s.pth) | | MobileMamba-S6 | 652M | 15.0M | 224 x 224 | 78.0 | [cfg](configs/mobilemamba/mobilemamba_s6.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_S6/mobilemamba_s6.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_S6/mobilemamba_s6.pth) | | MobileMamba-S6† | 652M | 15.0M | 224 x 224 | 80.7 | [cfg](configs/mobilemamba/mobilemamba_s6s.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_S6s/mobilemamba_s6s.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_S6s/mobilemamba_s6s.pth) | | MobileMamba-B1 | 1080M | 17.1M | 256 x 256 | 79.9 | [cfg](configs/mobilemamba/mobilemamba_b1.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B1/mobilemamba_b1.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B1/mobilemamba_b1.pth) | | MobileMamba-B1† | 1080M | 17.1M | 256 x 256 | 82.2 | [cfg](configs/mobilemamba/mobilemamba_b1s.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B1s/mobilemamba_b1s.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B1s/mobilemamba_b1s.pth) | | MobileMamba-B2 | 2427M | 17.1M | 384 x 384 | 81.6 | [cfg](configs/mobilemamba/mobilemamba_b2.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B2/mobilemamba_b2.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B2/mobilemamba_b2.pth) | | MobileMamba-B2† | 2427M | 17.1M | 384 x 384 | 83.3 | [cfg](configs/mobilemamba/mobilemamba_b2s.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B2s/mobilemamba_b2s.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B2s/mobilemamba_b2s.pth) | | MobileMamba-B4 | 4313M | 17.1M | 512 x 512 | 82.5 | [cfg](configs/mobilemamba/mobilemamba_b4.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B4/mobilemamba_b4.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B4/mobilemamba_b4.pth) | | MobileMamba-B4† | 4313M | 17.1M | 512 x 512 | 83.6 | [cfg](configs/mobilemamba/mobilemamba_b4s.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B4s/mobilemamba_b4s.txt) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/MobileMamba_B4s/mobilemamba_b4s.pth) | ------ # Downstream Results ## Object Detection and Instant Segmentation Results ### Object Detection and Instant Segmentation Performance Based on [Mask-RCNN](https://openaccess.thecvf.com/content_ICCV_2017/papers/He_Mask_R-CNN_ICCV_2017_paper.pdf) for [COCO2017](https://cocodataset.org): | Backbone | APb | APb50 | APb75 | APbS | APbM | APbL | APm | APm50 | APm75 | APmS | APmM | APmL | #Params | FLOPs | Cfg | Log | Model | |:--------:|:--------------:|:---------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------:|:---------------------------:|:---------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-------:|:-----:|:------------------------------------------:|:------------------------------------------:|:--------------------------------------------:| | MobileMamba-B1 | 40.6 | 61.8 | 43.8 | 22.4 | 43.5 | 55.9 | 37.4 | 58.9 | 39.9 | 17.1 | 39.9 | 56.4 | 38.0M | 178G | [cfg](downstream/det/configs/mask_rcnn/mask-rcnn_mobilemamba_b1_fpn_1x_coco.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/maskrcnn.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/maskrcnn.pth) | ### Object Detection Performance Based on [RetinaNet](https://openaccess.thecvf.com/content_ICCV_2017/papers/Lin_Focal_Loss_for_ICCV_2017_paper.pdf) for [COCO2017](https://cocodataset.org): | Backbone | AP | AP50 | AP75 | APS | APM | APL | #Params | FLOPs | Cfg | Log | Model | |:--------:|:----:|:---------------:|:---------------:|:--------------:|:--------------:|:--------------:|:-------:|:-----:|:-------------------------------------------:|:-------------------------------------------:|:---------------------------------------------:| | MobileMamba-B1 | 39.6 | 59.8 | 42.4 | 21.5 | 43.4 | 53.9 | 27.1M | 151G | [cfg](downstream/det/configs/retinanet/retinanet_mobilemamba_b1_fpn_1x_coco.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/retinanet.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/retinanet.pth) | ### Object Detection Performance Based on [SSDLite](https://openaccess.thecvf.com/content_ICCV_2019/papers/Howard_Searching_for_MobileNetV3_ICCV_2019_paper.pdf) for [COCO2017](https://cocodataset.org): | Backbone | AP | AP50 | AP75 | APS | APM | APL | #Params | FLOPs | Cfg | Log | Model | |:-------------------:|:----:|:---------------:|:---------------:|:--------------:|:--------------:|:--------------:|:-------:|:-----:|:-----------------------------------------------------------------------------:|:------------------------------:|:-----------------------------------------------:| | MobileMamba-B1 | 24.0 | 39.5 | 24.0 | 3.1 | 23.4 | 46.9 | 18.0M | 1.7G | [cfg](downstream/det/configs/ssd/ssdlite_mobilemamba_b1_8gpu_2lr_coco.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/ssdlite.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/ssdlite.pth) | | MobileMamba-B1-r512 | 29.5 | 47.7 | 30.4 | 8.9 | 35.0 | 47.0 | 18.0M | 4.4G | [cfg](downstream/det/configs/ssd/ssdlite_mobilemamba_b1_8gpu_2lr_512_coco.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/ssdlite_512.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/det/ssdlite_512.pth) | ## Semantic Segmentation Results ### Semantic Segmentation Based on [Semantic FPN](https://openaccess.thecvf.com/content_CVPR_2019/papers/Kirillov_Panoptic_Feature_Pyramid_Networks_CVPR_2019_paper.pdf) for [ADE20k](http://sceneparsing.csail.mit.edu/): | Backbone | aAcc | mIoU | mAcc | #Params | FLOPs | Cfg | Log | Model | |:--------:|:----:|:----:|:----:|:-------:|:-----:|:-------------------------------------:|:-------------------------------------:|:---------------------------------------:| | MobileMamba-B4 | 79.9 | 42.5 | 53.7 | 19.8M | 5.6G | [cfg](downstream/seg/configs/sem_fpn/fpn_mobilemamba_b4-160k_ade20k-512x512.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/seg/fpn.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/seg/fpn.pth) | ### Semantic Segmentation Based on [DeepLabv3](https://arxiv.org/pdf/1706.05587.pdf) for [ADE20k](http://sceneparsing.csail.mit.edu/): | Backbone | aAcc | mIoU | mAcc | #Params | FLOPs | Cfg | Log | Model | |:--------------:|:----:|:----:|:----:|:-------:|:-----:|:-------------------------------------------:|:-------------------------------------------:|:---------------------------------------------:| | MobileMamba-B4 | 76.3 | 36.6 | 47.1 | 23.4M | 4.7G | [cfg](downstream/seg/configs/deeplabv3/deeplabv3_mobilemamba_b4-80k_ade20k-512x512.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/seg/deeplabv3.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/seg/deeplabv3.pth) | ### Semantic Segmentation Based on [PSPNet](https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhao_Pyramid_Scene_Parsing_CVPR_2017_paper.pdf) for [ADE20k](http://sceneparsing.csail.mit.edu/): | Backbone | aAcc | mIoU | mAcc | #Params | FLOPs | Cfg | Log | Model | |:--------:|:----:|:----:|:----:|:-------:|:-----:|:----------------------------------------:|:----------------------------------------:|:------------------------------------------:| | MobileMamba-B4 | 76.2 | 36.9 | 47.9 | 20.5M | 4.5G | [cfg](downstream/seg/configs/pspnet/pspnet_mobilemamba_b4-80k_ade20k-512x512.py) | [log](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/seg/pspnet.log) | [model](https://huggingface.co/Lewandofski/MobileMamba/blob/main/downstream/seg/pspnet.pth) | ------ # All Pretrained Weights and Logs The model weights and log files for all classification and downstream tasks are available for download via [GoogleDrive](https://drive.google.com/file/d/1EDqWI6JKMaLZRSRWt9aM7VXaNvosStGE/view?usp=drive_link) and [Hugging Face](https://huggingface.co/Lewandofski/MobileMamba/tree/main).. ------ # Classification ## Environments ```shell pip3 install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 pip3 install timm==0.6.5 tensorboardX einops torchprofile fvcore==0.1.5.post20221221 cd model/lib_mamba/kernels/selective_scan && pip install . && cd ../../../.. git clone https://github.com/NVIDIA/apex && cd apex && pip3 install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ (optional) ``` ## Prepare ImageNet-1K Dataset Download and extract [ImageNet-1K](http://image-net.org/) dataset in the following directory structure: ``` ├── imagenet ├── train ├── n01440764 ├── n01440764_10026.JPEG ├── ... ├── ... ├── train.txt (optional) ├── val ├── n01440764 ├── ILSVRC2012_val_00000293.JPEG ├── ... ├── ... └── val.txt (optional) ``` ## Test Test with 8 GPUs in one node:
MobileMamba-T2 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t2 -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_T2/mobilemamba_t2.pth ``` This should give `Top-1: 73.638 (Top-5: 91.422)`
MobileMamba-T2† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t2s -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_T2s/mobilemamba_t2s.pth ``` This should give `Top-1: 76.934 (Top-5: 93.100)`
MobileMamba-T4 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t4 -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_T4/mobilemamba_t4.pth ``` This should give `Top-1: 76.086 (Top-5: 92.772)`
MobileMamba-T4† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t4s -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_T4s/mobilemamba_t4s.pth ``` This should give `Top-1: 78.914 (Top-5: 94.160)`
MobileMamba-S6 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_s6 -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_S6/mobilemamba_s6.pth ``` This should give `Top-1: 78.002 (Top-5: 93.992)`
MobileMamba-S6† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_s6s -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_S6s/mobilemamba_s6s.pth ``` This should give `Top-1: 80.742 (Top-5: 95.182)`
MobileMamba-B1 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b1 -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_B1/mobilemamba_b1.pth ``` This should give `Top-1: 79.948 (Top-5: 94.924)`
MobileMamba-B1† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b1s -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_B1s/mobilemamba_b1s.pth ``` This should give `Top-1: 82.234 (Top-5: 95.872)`
MobileMamba-B2 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b2 -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_B2/mobilemamba_b2.pth ``` This should give `Top-1: 81.624 (Top-5: 95.890)`
MobileMamba-B2† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b2s -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_B2s/mobilemamba_b2s.pth ``` This should give `Top-1: 83.260 (Top-5: 96.438)`
MobileMamba-B4 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b4 -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_B4/mobilemamba_b4.pth ``` This should give `Top-1: 82.496 (Top-5: 96.252)`
MobileMamba-B4† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b4s -m test model.model_kwargs.checkpoint_path=weights/MobileMamba_B4s/mobilemamba_b4s.pth ``` This should give `Top-1: 83.644 (Top-5: 96.606)`
## Train Train with 8 GPUs in one node:
MobileMamba-T2 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t2 -m train ```
MobileMamba-T2† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t2s -m train ```
MobileMamba-T4 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t4 -m train ```
MobileMamba-T4† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_t4s -m train ```
MobileMamba-S6 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_s6 -m train ```
MobileMamba-S6† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_s6s -m train ```
MobileMamba-B1 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b1 -m train ```
MobileMamba-B1† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b1s -m train ```
MobileMamba-B2 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b2 -m train ```
MobileMamba-B2† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b2s -m train ```
MobileMamba-B4 ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b4 -m train ```
MobileMamba-B4† ``` python3 -m torch.distributed.launch --nproc_per_node=8 --nnodes=1 --use_env run.py -c configs/mobilemamba/mobilemamba_b4s -m train ```
------ # Down-Stream Tasks ## Environments ```shell pip3 install terminaltables pycocotools prettytable xtcocotools pip3 install mmpretrain==1.2.0 mmdet==3.3.0 mmsegmentation==1.2.2 pip3 install mmcv==2.1.0 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.1/index.html cd det/backbones/lib_mamba/kernels/selective_scan && pip install . && cd ../../../.. ``` ## Prepare COCO and ADE20k Dataset Download and extract [COCO2017](https://cocodataset.org) and [ADE20k](http://sceneparsing.csail.mit.edu/) dataset in the following directory structure: ``` downstream ├── det ├──── data │ ├──── coco │ │ ├──── annotations │ │ ├──── train2017 │ │ ├──── val2017 │ │ ├──── test2017 ├── seg ├──── data │ ├──── ade │ │ ├──── ADEChallengeData2016 │ │ ├──────── annotations │ │ ├──────── images ``` ## Object Detection
Mask-RCNN #### Train: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/mask_rcnn/mask-rcnn_mobilemamba_b1_fpn_1x_coco.py 4 ``` #### Test: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh configs/mask_rcnn/mask-rcnn_mobilemamba_b1_fpn_1x_coco.py ../../weights/downstream/det/maskrcnn.pth 4 ```
RetinaNet #### Train: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/retinanet/retinanet_mobilemamba_b1_fpn_1x_coco.py 4 ``` #### Test: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh configs/retinanet/retinanet_mobilemamba_b1_fpn_1x_coco.py ../../weights/downstream/det/retinanet.pth 4 ```
SSDLite #### Train with 320 x 320 resolution: ``` ./tools/dist_train.sh configs/ssd/ssdlite_mobilemamba_b1_8gpu_2lr_coco.py 8 ``` #### Test with 320 x 320 resolution: ``` ./tools/dist_test.sh configs/ssd/ssdlite_mobilemamba_b1_8gpu_2lr_coco.py ../../weights/downstream/det/ssdlite.pth 8 ``` #### Train with 512 x 512 resolution: ``` ./tools/dist_train.sh configs/ssd/ssdlite_mobilemamba_b1_8gpu_2lr_512_coco.py 8 ``` #### Test with 512 x 512 resolution: ``` ./tools/dist_test.sh configs/ssd/ssdlite_mobilemamba_b1_8gpu_2lr_512_coco.py ../../weights/downstream/det/ssdlite_512.pth 8 ```
## Semantic Segmentation
DeepLabV3 #### Train: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/deeplabv3/deeplabv3_mobilemamba_b4-80k_ade20k-512x512.py 4 ``` #### Test: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh configs/deeplabv3/deeplabv3_mobilemamba_b4-80k_ade20k-512x512.py ../../weights/downstream/seg/deeplabv3.pth 4 ```
Semantic FPN #### Train: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/sem_fpn/fpn_mobilemamba_b4-160k_ade20k-512x512.py 4 ``` #### Test: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh configs/sem_fpn/fpn_mobilemamba_b4-160k_ade20k-512x512.py ../../weights/downstream/seg/fpn.pth 4 ```
PSPNet #### Train: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_train.sh configs/pspnet/pspnet_mobilemamba_b4-80k_ade20k-512x512.py 4 ``` #### Test: ``` CUDA_VISIBLE_DEVICES=0,1,2,3 ./tools/dist_test.sh configs/pspnet/pspnet_mobilemamba_b4-80k_ade20k-512x512.py ../../weights/downstream/seg/pspnet.pth 4 ```
# Citation If our work is helpful for your research, please consider citing: ```angular2html @article{mobilemamba, title={MobileMamba: Lightweight Multi-Receptive Visual Mamba Network}, author={Haoyang He and Jiangning Zhang and Yuxuan Cai and Hongxu Chen and Xiaobin Hu and Zhenye Gan and Yabiao Wang and Chengjie Wang and Yunsheng Wu and Lei Xie}, journal={arXiv preprint arXiv:2411.15941}, year={2024} } ``` # Acknowledgements We thank but not limited to following repositories for providing assistance for our research: - [EMO](https://github.com/zhangzjn/EMO) - [EfficientViT](https://github.com/microsoft/Cream/tree/main/EfficientViT) - [VMamba](https://github.com/MzeroMiko/VMamba) - [TIMM](https://github.com/rwightman/pytorch-image-models) - [MMDetection](https://github.com/open-mmlab/mmdetection) - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation)