Lightweight Model for Real-Time Semantic Segmentation
This project aims at providing the popular lightweight model implementations for real-time semantic segmentation.
Usage
Train
- Single GPU training
python train.py --model mobilenet --dataset citys --lr 0.01 --epochs 240
- Multi-GPU training
# for example, train mobilenet with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --model mobilenet --dataset citys --lr 0.01 --epochs 240
Evaluation
- Single GPU evaluating
python eval.py --model mobilenet_small --dataset citys
- Multi-GPU evaluating
# for example, evaluate mobilenet with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS eval.py --model mobilenet --dataset citys
Result
- Cityscapes
Where: crop_size=768, lr=0.01, epochs=80.
| Backbone | OHEM | Params(M) | FLOPs(G) | CPU(fps) | GPU(fps) | mIoU/pixACC | Model |
|---|---|---|---|---|---|---|---|
| mobilenet | ✘ | 5.31 | 4.48 | 0.81 | 77.11 | 0.463/0.901 | GoogleDrive,BaiduCloud(ybsg) |
| mobilenet | ✓ | 5.31 | 4.48 | 0.81 | 75.35 | 0.526/0.909 | GoogleDrive,BaiduCloud(u2y2) |
| mobilenetv2 | ✓ | 4.88 | 4.04 | 0.49 | 49.40 | 0.613/0.930 | GoogleDrive,BaiduCloud(q2g5) |
| mobilenetv3_small | ✓ | 1.02 | 1.64 | 2.59 | 104.56 | 0.529/0.908 | GoogleDrive,BaiduCloud(e7no) |
| mobilenetv3_large | ✓ | 2.68 | 4.59 | 1.39 | 79.43 | 0.584/0.916 | GoogleDrive,BaiduCloud(i60c) |
| shufflenet | ✓ | 6.89 | 5.68 | 0.57 | 43.79 | 0.493/0.901 | GoogleDrive,BaiduCloud(6fjh) |
| shufflenetv2 | ✓ | 5.24 | 4.33 | 0.72 | 57.71 | 0.528/0.914 | GoogleDrive,BaiduCloud(7pi5) |
| igcv3 | ✓ | 4.86 | 4.04 | 0.34 | 29.70 | 0.573/0.923 | GoogleDrive,BaiduCloud(qe4f) |
| efficientnet-b0 | ✓ | 6.63 | 2.60 | 0.33 | 30.15 | 0.492/0.903 | GoogleDrive,BaiduCloud(phuy) |
- Improve
| Model | batch_size | epochs | crop_size | init_weight | optimizer | mIoU/pixACC |
|---|---|---|---|---|---|---|
| mobilenetv3_small | 4 | 80 | 768 | kaiming_uniform | SGD | 0.529/0.908 |
| mobilenetv3_small | 4 | 160 | 768 | kaiming_uniform | SGD | 0.587/0.918 |
| mobilenetv3_small | 8 | 160 | 768 | kaiming_uniform | SGD | 0.553/0/913 |
| mobilenetv3_small | 4 | 80 | 1024 | kaiming_uniform | SGD | 0.557/0.914 |
| mobilenetv3_small | 4 | 80 | 768 | xavier_uniform | SGD | 0.550/0.911 |
| mobilenetv3_small | 4 | 80 | 768 | kaiming_uniform | Adam | 0.549/0.911 |
| mobilenetv3_small | 8 | 160 | 1024 | xavier_uniform | SGD | 0.612/0.920 |
Support
To Do
- improve performance
- optimize memory
- check efficientnet
- replace
nn.SyncBatchNormbynn.BatchNorm.convert_sync_batchnorm - check
find_unused_parametersinnn.parallel.DistributedDataParallel