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Pytorch implementation of EfficientNet-lite. ImageNet pre-trained models are provided.

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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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Pytorch implementation of EfficientNet-lite. ImageNet pre-trained models are provided.

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