Skip to content

Latest commit

 

History

History
83 lines (55 loc) · 2.71 KB

README.md

File metadata and controls

83 lines (55 loc) · 2.71 KB

MobileNet

A tensorflow implementation of Google's MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Base Module



Accuracy on ImageNet-2012 Validation Set

Model Width Multiplier Preprocessing Accuracy-Top1 Accuracy-Top5
MobileNet 1.0 Same as Inception 66.51% 87.09%

Click to download the pretrained weight.

Loss



Time Benchmark

Environment: Ubuntu 16.04 LTS, Xeon E3-1231 v3, 4 Cores @ 3.40GHz, GTX1060.

TF 1.0.1(native pip install), TF 1.1.0(build from source, optimization flag '-mavx2')

Input image size: (224, 224, 3)

Batch size: 1

Device Forward Forward-Backward Instruction set Quantized Fused-BN Remark
CPU 52ms 503ms - - - TF 1.0.1
CPU 44ms 177ms - - On TF 1.0.1
CPU 31ms - - 8-bits - TF 1.0.1
CPU 26ms 75ms AVX2 - - TF 1.1.0
CPU 128ms - AVX2 8-bits - TF 1.1.0
CPU 19ms 89ms AVX2 - On TF 1.1.0
GPU 3ms 16ms - - - TF 1.0.1, CUDA8.0, CUDNN5.1
GPU 3ms 15ms - - On TF 1.0.1, CUDA8.0, CUDNN5.1

Usage

Train on Imagenet

  1. Prepare imagenet data. Please refer to Google's tutorial for training inception.

  2. Modify './script/train_mobilenet_on_imagenet.sh' according to your environment.

bash ./script/train_mobilenet_on_imagenet.sh

Benchmark speed

python time_benchmark.py

Trouble Shooting

  1. About the MobileNet model size

According to the paper, MobileNet has 3.3 Million Parameters, which does not vary based on the input resolution. It means that the number of final model parameters should be larger than 3.3 Million, because of the fc layer.

When using RMSprop training strategy, the checkpoint file size should be almost 3 times as large as the model size, because of some auxiliary parameters used in RMSprop. You can use the inspect_checkpoint.py to figure it out.

  1. Pretrained weight

Welcome to share if you had trained a better model.

TODO

  • Train on Imagenet
  • Add Width Multiplier Hyperparameters
  • Report training result
  • Intergrate into object detection task

Reference

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

SSD-Tensorflow