wenlisong / mobilenetv3

mobilenetv3 with pytorch，provide pre-train model

A PyTorch implementation of MobileNetV3

I retrain the mobilenetv3 with some novel tricks and timm. I also provide the train code, pre-training weight and training logs on this project.

You should use torch.load to load the model.

from mobilenetv3 import MobileNetV3_Small, MobileNetV3_Large

# MobileNetV3_Small
net = MobileNetV3_Small()
net.load_state_dict(torch.load("450_act3_mobilenetv3_small.pth", map_location='cpu'))

# MobileNetV3_Large
net = MobileNetV3_Large()
net.load_state_dict(torch.load("450_act3_mobilenetv3_large.pth", map_location='cpu'))

You could reproduce the model by the code.

nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_small --epochs 300 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint &

nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_small --epochs 450 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint &

nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_large --epochs 300 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint &

nohup python -u -m torch.distributed.run --nproc_per_node=8 main.py --model mobilenet_v3_large --epochs 450 --batch_size 256 --lr 4e-3 --update_freq 2 --model_ema false --model_ema_eval false --use_amp true --data_path /data/benchmarks/ILSVRC2012 --output_dir ./checkpoint &

This is a PyTorch implementation of MobileNetV3 architecture as described in the paper Searching for MobileNetV3.

Some details may be different from the original paper, welcome to discuss and help me figure it out.

MobileNetV3

	Madds	Parameters	Top1-acc
Small (paper)	66 M	2.9 M	67.4%
Small (torchvision)	62 M	2.5 M	67.7%
Small (our 300 epoch)	69 M	3.0 M	68.9%
Small (our 450 epoch)	69 M	3.0 M	69.2%
Large (paper)	219 M	5.4 M	75.2%
Large (torchvision)	235 M	5.5 M	74.0%
Large (our 300 epoch)	241 M	5.2 M	75.6%
Large (our 450 epoch)	241 M	5.2 M	75.9%