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')
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
-
Prepare imagenet data. Please refer to Google's tutorial for training inception.
-
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
- 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.
- Slim multi-gpu performance problems
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