Tramac/mobilenetv3-segmentation

MobileNetV3 for Semantic Segmentation.

mobilenetv3-segmentation

An unofficial implement of MobileNetV3 for semantic segmentation.

Requisites

• PyTorch 1.1
• Python 3.x

Usage

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Train

• Single GPU training

python train.py --model mobilenetv3_small --dataset citys --lr 0.0001 --epochs 240

• Multi-GPU training

# for example, train mobilenetv3 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --model mobilenetv3_small --dataset citys --lr 0.0001 --epochs 240

Evaluation

• Single GPU training

python eval.py --model mobilenetv3_small --dataset citys

• Multi-GPU training

# for example, evaluate mobilenetv3 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS --model mobilenetv3_small --dataset citys

Result

• Cityscapes

Backbone	F	Epochs	OHEM	mIoU	Params(M)	Madds(G)	CPU(fps)	GPU(fps)
MV3-Small	128	80	✘	0.411	1.02	2.98	1.12	76.61
MV3-Small	128	80	✓	0.476	-	-	-	-
MV3-Large	128	80	✘	0.463	2.68	8.40	0.61	63.16
MV3-Large	128	80	✓	0.529	-	-	-	-
MV3-Large	128	160	✓	0.526	-	-	-	-

where: lr=0.01, crop_size=768

Note: Params and Madds are got using torchscope. They are much larger than those reported in the paper.

To Do

• improve performance
• train and eval
• test madds

References

• maskrcnn-benchmark