mindspore/model_zoo/official/cv/yolov3_resnet18

• Contents
• YOLOv3_ResNet18 Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
• • Script and Sample Code
  • Script Parameters
  • Training Process
  • • Training on Ascend
  • Evaluation Process
  • • Evaluation on Ascend
  • Export MindIR
  • Inference Process
  • • Usage
    • result
• Model Description
• • Performance
  • • Evaluation Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

Contents

• YOLOv3_ResNet18 Description
• Model Architecture
• Dataset
• Environment Requirements
• Quick Start
• Script Description
  • Script and Sample Code
  • Script Parameters
  • Training Process
    • Training
  • Evaluation Process
    • Evaluation
  • Export MindIR
  • Inference Process
• Model Description
  • Performance
    • Evaluation Performance
    • Inference Performance
• Description of Random Situation
• ModelZoo Homepage

YOLOv3_ResNet18 Description

YOLOv3 network based on ResNet-18, with support for training and evaluation.

Paper: Joseph Redmon, Ali Farhadi. arXiv preprint arXiv:1804.02767, 2018. 2, 4, 7, 11.

Model Architecture

The overall network architecture of YOLOv3 is show below:

And we use ResNet18 as the backbone of YOLOv3_ResNet18. The architecture of ResNet18 has 4 stages. The ResNet architecture performs the initial convolution and max-pooling using 7×7 and 3×3 kernel sizes respectively. Afterward, every stage of the network has different Residual blocks (2, 2, 2, 2) containing two 3×3 conv layers. Finally, the network has an Average Pooling layer followed by a fully connected layer.

Dataset

Note that you can run the scripts based on the dataset mentioned in original paper or widely used in relevant domain/network architecture. In the following sections, we will introduce how to run the scripts using the related dataset below.

Dataset used: COCO2017

• Dataset size：19G

  • Train：18G，118000 images
  • Val：1G，5000 images
  • Annotations：241M，instances，captions，person_keypoints etc

• Data format：image and json files

  • Note：Data will be processed in dataset.py

• Dataset

  1. The directory structure is as follows:

     .
     ├── annotations  # annotation jsons
     ├── train2017    # train dataset
     └── val2017      # infer dataset
     

  2. Organize the dataset information into a TXT file, each row in the file is as follows:

     train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
     

     Each row is an image annotation which split by space, the first column is a relative path of image, the others are box and class infomations of the format [xmin,ymin,xmax,ymax,class]. dataset.py is the parsing script, we read image from an image path joined by the image_dir(dataset directory) and the relative path in anno_path(the TXT file path), image_dir and anno_path are external inputs.

Environment Requirements

• Hardware（Ascend）
  • Prepare hardware environment with Ascend processor.
• Framework
  • MindSpore
• For more information, please check the resources below：
  • MindSpore Tutorials
  • MindSpore Python API

Quick Start

After installing MindSpore via the official website, you can start training and evaluation on Ascend as follows:

• running on Ascend

  #run standalone training example
  sh run_standalone_train.sh [DEVICE_ID] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH]
  
  #run distributed training example
  sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH] [RANK_TABLE_FILE]
  
  #run evaluation example
  sh run_eval.sh [DEVICE_ID] [CKPT_PATH] [MINDRECORD_DIR] [IMAGE_DIR] [ANNO_PATH]
  

Script Description

Script and Sample Code

└── cv
    ├── README.md                           // descriptions about all the models
    ├── mindspore_hub_conf.md               // config for mindspore hub
    └── yolov3_resnet18
        ├── README.md                       // descriptions about yolov3_resnet18
        ├── scripts
            ├── run_distribute_train.sh     // shell script for distributed on Ascend
            ├── run_standalone_train.sh     // shell script for distributed on Ascend
            └── run_eval.sh                 // shell script for evaluation on Ascend
        ├── src
            ├── dataset.py                  // creating dataset
            ├── yolov3.py                   // yolov3 architecture
            ├── config.py                   // parameter configuration
            └── utils.py                    // util function
        ├── train.py                        // training script
        └── eval.py                         // evaluation script  

Script Parameters

Major parameters in train.py and config.py as follows:

  device_num: Use device nums, default is 1.
  lr: Learning rate, default is 0.001.
  epoch_size: Epoch size, default is 50.
  batch_size: Batch size, default is 32.
  pre_trained: Pretrained Checkpoint file path.
  pre_trained_epoch_size: Pretrained epoch size.
  mindrecord_dir: Mindrecord directory.
  image_dir: Dataset path.
  anno_path: Annotation path.

  img_shape: Image height and width used as input to the model.

Training Process

Training on Ascend

To train the model, run train.py with the dataset image_dir, anno_path and mindrecord_dir. If the mindrecord_dir is empty, it wil generate mindrecord file by image_dir and anno_path(the absolute image path is joined by the image_dir and the relative path in anno_path). Note if mindrecord_dir isn't empty, it will use mindrecord_dir rather than image_dir and anno_path.

• Stand alone mode

  sh run_standalone_train.sh 0 50 ./Mindrecord_train ./dataset ./dataset/train.txt
  

  The input variables are device id, epoch size, mindrecord directory path, dataset directory path and train TXT file path.

• Distributed mode

  sh run_distribute_train.sh 8 150 /data/Mindrecord_train /data /data/train.txt /data/hccl.json
  

  The input variables are device numbers, epoch size, mindrecord directory path, dataset directory path, train TXT file path and hccl json configuration file. It is better to use absolute path.

You will get the loss value and time of each step as following:

epoch: 145 step: 156, loss is 12.202981
epoch time: 25599.22742843628, per step time: 164.0976117207454
epoch: 146 step: 156, loss is 16.91706
epoch time: 23199.971675872803, per step time: 148.7177671530308
epoch: 147 step: 156, loss is 13.04007
epoch time: 23801.95164680481, per step time: 152.57661312054364
epoch: 148 step: 156, loss is 10.431475
epoch time: 23634.241580963135, per step time: 151.50154859591754
epoch: 149 step: 156, loss is 14.665991
epoch time: 24118.8325881958, per step time: 154.60790120638333
epoch: 150 step: 156, loss is 10.779521
epoch time: 25319.57221031189, per step time: 162.30495006610187

Note the results is two-classification(person and face) used our own annotations with coco2017, you can change num_classes in config.py to train your dataset. And we will support 80 classifications in coco2017 the near future.

Evaluation Process

Evaluation on Ascend

To eval, run eval.py with the dataset image_dir, anno_path(eval txt), mindrecord_dir and ckpt_path. ckpt_path is the path of checkpoint file.

sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt

The input variables are device id, checkpoint path, mindrecord directory path, dataset directory path and train TXT file path.

You will get the precision and recall value of each class:

class 0 precision is 88.18%, recall is 66.00%
class 1 precision is 85.34%, recall is 79.13%

Note the precision and recall values are results of two-classification(person and face) used our own annotations with coco2017.

Export MindIR

Currently, batchsize can only set to 1.

python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --file_format [FILE_FORMAT]

The ckpt_file parameter is required,
EXPORT_FORMAT should be in ["AIR", "MINDIR"]

Inference Process

Usage

Before performing inference, the mindir file must be exported by export.py.
Current batch_Size can only be set to 1. Images to be processed needs to be copied to the to-be-processed folder based on the annotation file.

# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [DATA_PATH] [ANNO_PATH] [DEVICE_ID]

DEVICE_ID is optional, default value is 0.

result

Inference result is saved in current path, you can find result in acc.log file.

class 0 precision is 88.18%, recall is 66.00%
class 1 precision is 85.34%, recall is 79.13%

Model Description

Performance

Evaluation Performance

Parameters	Ascend
Model Version	YOLOv3_Resnet18 V1
Resource	Ascend 910; CPU 2.60GHz, 192cores; Memory 755G; OS Euler2.8
uploaded Date	09/15/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	COCO2017
Training Parameters	epoch = 150, batch_size = 32, lr = 0.001
Optimizer	Adam
Loss Function	Sigmoid Cross Entropy
outputs	probability
Speed	1pc: 120 ms/step; 8pcs: 160 ms/step
Total time	1pc: 150 mins; 8pcs: 70 mins
Parameters (M)	189
Scripts	yolov3_resnet18 script

Inference Performance

Parameters	Ascend
Model Version	YOLOv3_Resnet18 V1
Resource	Ascend 910; OS Euler2.8
Uploaded Date	09/15/2020 (month/day/year)
MindSpore Version	1.0.0
Dataset	COCO2017
batch_size	1
outputs	presion and recall
Accuracy	class 0: 88.18%/66.00%; class 1: 85.34%/79.13%

Description of Random Situation

In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.

ModelZoo Homepage

Please check the official homepage.