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%

Download the pretrained weight: OneDrive, BaiduYun

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')

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

Image Size: (224, 224, 3), Batch Size: 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 ./scripts/time_benchmark.py

Train MobileNet Detector (Debugging)

1. Prepare KITTI data.

After download KITTI data, you need to split it data into train/val set.

cd /path/to/kitti_root
mkdir ImageSets
cd ./ImageSets
ls ../training/image_2/ | grep ".png" | sed s/.png// > trainval.txt
python ./tools/kitti_random_split_train_val.py

kitti_root floder then look like below

kitti_root/
                  |->training/
                  |     |-> image_2/00****.png
                  |     L-> label_2/00****.txt
                  |->testing/
                  |     L-> image_2/00****.png
                  L->ImageSets/
                        |-> trainval.txt
                        |-> train.txt
                        L-> val.txt

Then convert it into tfrecord.

python ./tools/tf_convert_data.py

2. Mobify './script/train_mobilenet_on_kitti.sh' according to your environment.

bash ./script/train_mobilenetdet_on_kitti.sh

The code of this subject is largely based on SqueezeDet & SSD-Tensorflow. I would appreciated if you could feed back any bug.

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.

2. Slim multi-gpu performance problems

#1390 #1428

TODO

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

Reference

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

SSD-Tensorflow

SqueezeDet