efficientnet-lite's Introduction

EfficientNet-Lite Pytorch

Pytorch implementation of Google's EfficientNet-lite. Provide imagenet pre-train models.

In EfficientNet-Lite, all SE modules are removed and all swish layers are replaced with ReLU6. It's more friendly for edge devices than EfficientNet-B series.

Model details:

Model	Params	MAdds	Top1 Acc(Official)	Top1 Acc(This repo)	Top5 Acc
efficientnet-lite0	4.7M	407M	75.1%	71.73%	90.17%
efficientnet-lite1	5.4M	631M	76.7%	74.71%	92.01%
efficientnet-lite2	6.1M	899M	77.6%	77.14%	93.54%
efficientnet-lite3	8.2M	1.44B	79.8%	78.91%	94.37%
efficientnet-lite4	13.0M	2.64B	81.5%	80.34%	95.06%

Download Model

Pre-train Model
efficientnet-lite0 Download Link
efficientnet-lite1 Download Link
efficientnet-lite2 Download Link
efficientnet-lite3 Download Link
efficientnet-lite4 Download Link

Train

python train.py --model_name efficientnet_lite0 --train_dir YOUR_TRAINDATASET_PATH --val_dir YOUR_VALDATASET_PATH

Eval

python train.py --eval --eval_resume YOUR_MODEL_PATH --model_name efficientnet_lite0  --train_dir YOUR_TRAINDATASET_PATH --val_dir YOUR_VALDATASET_PATH

eval reaults:

efficientnet_lite0
TEST Iter 0: loss = 2.100231,     Top-1 err = 0.282700,   Top-5 err = 0.098280,   val_time = 120.648957

efficientnet_lite1
TEST Iter 0: loss = 2.076898,     Top-1 err = 0.252940,   Top-5 err = 0.079880,   val_time = 126.869352

efficientnet_lite2
TEST Iter 0: loss = 1.929238,     Top-1 err = 0.228660,   Top-5 err = 0.064640,   val_time = 142.668548

efficientnet_lite3
TEST Iter 0: loss = 1.782202,     Top-1 err = 0.210920,   Top-5 err = 0.056260,   val_time = 147.359098

efficientnet_lite4
TEST Iter 0: loss = 1.714834,     Top-1 err = 0.196580,   Top-5 err = 0.049440,   val_time = 158.336004

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