mindspore/model_zoo/research/cv/FaceRecognitionForTracking

• Contents
• Face Recognition For Tracking Description
• Model Architecture
• Dataset
• Environment Requirements
• Script Description
• • Script and Sample Code
  • Running Example
  • • Train
    • Evaluation
    • Convert model
• Model Description
• • Performance
  • • Training Performance
    • Evaluation Performance
• ModelZoo Homepage

Contents

• Face Recognition For Tracking Description
• Model Architecture
• Dataset
• Environment Requirements
• Script Description
  • Script and Sample Code
  • Running Example
• Model Description
  • Performance
• ModelZoo Homepage

Face Recognition For Tracking Description

This is a face recognition for tracking network based on Resnet, with support for training and evaluation on Ascend910.

ResNet (residual neural network) was proposed by Kaiming He and other four Chinese of Microsoft Research Institute. Through the use of ResNet unit, it successfully trained 152 layers of neural network, and won the championship in ilsvrc2015. The error rate on top 5 was 3.57%, and the parameter quantity was lower than vggnet, so the effect was very outstanding. Traditional convolution network or full connection network will have more or less information loss. At the same time, it will lead to the disappearance or explosion of gradient, which leads to the failure of deep network training. ResNet solves this problem to a certain extent. By passing the input information to the output, the integrity of the information is protected. The whole network only needs to learn the part of the difference between input and output, which simplifies the learning objectives and difficulties.The structure of ResNet can accelerate the training of neural network very quickly, and the accuracy of the model is also greatly improved. At the same time, ResNet is very popular, even can be directly used in the concept net network.

Paper: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. "Deep Residual Learning for Image Recognition"

Model Architecture

Face Recognition For Tracking uses a Resnet network for performing feature extraction.

Dataset

We use about 10K face images as training dataset and 2K as evaluating dataset in this example, and you can also use your own datasets or open source datasets (e.g. Labeled Faces in the Wild)
The directory structure is as follows:

.
└─ dataset
  ├─ train dataset
    ├─ ID1
      ├─ ID1_0001.jpg
      ├─ ID1_0002.jpg
      ...
    ├─ ID2
      ...
    ├─ ID3
      ...
    ...
  ├─ test dataset
    ├─ ID1
      ├─ ID1_0001.jpg
      ├─ ID1_0002.jpg
      ...
    ├─ ID2
      ...
    ├─ ID3
      ...
    ...

Environment Requirements

• Hardware(Ascend)
  • Prepare hardware environment with Ascend processor. If you want to try Ascend, please send the application form to ascend@huawei.com. Once approved, you can get the resources.
• Framework
  • MindSpore
• For more information, please check the resources below:
  • MindSpore tutorials
  • MindSpore Python API

Script Description

Script and Sample Code

The entire code structure is as following:

.
└─ Face Recognition For Tracking
  ├─ README.md
  ├─ scripts
    ├─ run_standalone_train.sh              # launch standalone training(1p) in ascend
    ├─ run_distribute_train.sh              # launch distributed training(8p) in ascend
    ├─ run_eval.sh                          # launch evaluating in ascend
    └─ run_export.sh                        # launch exporting air model
  ├─ src
    ├─ config.py                            # parameter configuration
    ├─ dataset.py                           # dataset loading and preprocessing for training
    ├─ reid.py                              # network backbone
    ├─ reid_for_export.py                   # network backbone for export
    ├─ log.py                               # log function
    ├─ loss.py                              # loss function
    ├─ lr_generator.py                      # generate learning rate
    └─ me_init.py                           # network initialization
  ├─ train.py                               # training scripts
  ├─ eval.py                                # evaluation scripts
  └─ export.py                              # export air model

Running Example

Train

• Stand alone mode

  cd ./scripts
  sh run_standalone_train.sh [DATA_DIR] [USE_DEVICE_ID]
  

  or (fine-tune)

  cd ./scripts
  sh run_standalone_train.sh [DATA_DIR] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]
  

  for example:

  cd ./scripts
  sh run_standalone_train.sh /home/train_dataset 0 /home/a.ckpt
  

• Distribute mode (recommended)

  cd ./scripts
  sh run_distribute_train.sh [DATA_DIR] [RANK_TABLE]
  

  or (fine-tune)

  cd ./scripts
  sh run_distribute_train.sh [DATA_DIR] [RANK_TABLE] [PRETRAINED_BACKBONE]
  

  for example:

  cd ./scripts
  sh run_distribute_train.sh /home/train_dataset ./rank_table_8p.json /home/a.ckpt
  

You will get the loss value of each step as following in "./output/[TIME]/[TIME].log" or "./scripts/device0/train.log":

epoch[0], iter[10], loss:43.314265, 8574.83 imgs/sec, lr=0.800000011920929
epoch[0], iter[20], loss:45.121095, 8915.66 imgs/sec, lr=0.800000011920929
epoch[0], iter[30], loss:42.342847, 9162.85 imgs/sec, lr=0.800000011920929
epoch[0], iter[40], loss:39.456583, 9178.83 imgs/sec, lr=0.800000011920929

...
epoch[179], iter[14900], loss:1.651353, 13001.25 imgs/sec, lr=0.02500000037252903
epoch[179], iter[14910], loss:1.532123, 12669.85 imgs/sec, lr=0.02500000037252903
epoch[179], iter[14920], loss:1.760322, 13457.81 imgs/sec, lr=0.02500000037252903
epoch[179], iter[14930], loss:1.694281, 13417.38 imgs/sec, lr=0.02500000037252903

Evaluation

cd ./scripts
sh run_eval.sh [EVAL_DIR] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]

for example:

cd ./scripts
sh run_eval.sh /home/test_dataset 0 /home/a.ckpt

You will get the result as following in "./scripts/device0/eval.log" or txt file in [PRETRAINED_BACKBONE]'s folder:

0.5: 0.9273788254649683@0.020893691253149882
0.3: 0.8393850978779193@0.07438552515516506
0.1: 0.6220871197028316@0.1523084478903911
0.01: 0.2683641598437038@0.26217882879427634
0.001: 0.11060269148211463@0.34509718987101223
0.0001: 0.05381678898728808@0.4187797093636618
1e-05: 0.035770748447963394@0.5053771466191392

Convert model

If you want to infer the network on Ascend 310, you should convert the model to AIR:

cd ./scripts
sh run_export.sh [BATCH_SIZE] [USE_DEVICE_ID] [PRETRAINED_BACKBONE]

Model Description

Performance

Training Performance

Parameters	Face Recognition For Tracking
Model Version	V1
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory, 755G
uploaded Date	09/30/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	10K images
Training Parameters	epoch=180, batch_size=16, momentum=0.9
Optimizer	Momentum
Loss Function	Softmax Cross Entropy
outputs	probability
Speed	1pc: 8~10 ms/step; 8pcs: 9~11 ms/step
Total time	1pc: 1 hours; 8pcs: 0.1 hours
Checkpoint for Fine tuning	17M (.ckpt file)

Evaluation Performance

Parameters	Face Recognition For Tracking
Model Version	V1
Resource	Ascend 910
Uploaded Date	09/30/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	2K images
batch_size	128
outputs	recall
Recall(8pcs)	0.62(FAR=0.1)
Model for inference	17M (.ckpt file)

ModelZoo Homepage

Please check the official homepage.