mindspore/model_zoo/ssd

• SSD Example
• • Description
  • Requirements
  • Running the example
  • • Training
    • Evaluation

SSD Example

Description

SSD network based on MobileNetV2, with support for training and evaluation.

Requirements

• Install MindSpore.

• Dataset

  We use coco2017 as training dataset in this example by default, and you can also use your own datasets.

  1. If coco dataset is used. Select dataset to coco when run script.
     Install Cython and pycocotool.

     pip install Cython
     
     pip install pycocotools
     

     And change the coco_root and other settings you need in config.py. The directory structure is as follows:

     .
     └─cocodataset
       ├─annotations
         ├─instance_train2017.json
         └─instance_val2017.json
       ├─val2017
       └─train2017
     

  2. If your own dataset is used. Select dataset to other when run script.
     Organize the dataset infomation 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]. 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 setting in config.py.

Running the example

Training

To train the model, run train.py. If the mindrecord_dir is empty, it will generate mindrecord files by coco_root(coco dataset) or iamge_dir and anno_path(own dataset). Note if mindrecord_dir isn't empty, it will use mindrecord_dir instead of raw images.

• Stand alone mode

  python train.py --dataset coco
  
  

  You can run python train.py -h to get more information.

• Distribute mode

  sh run_distribute_train.sh 8 500 0.2 coco /data/hccl.json
  

  The input parameters are device numbers, epoch size, learning rate, dataset mode and hccl json configuration file. It is better to use absolute path.

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

epoch: 1 step: 458, loss is 3.1681802
epoch time: 228752.4654865265, per step time: 499.4595316299705
epoch: 2 step: 458, loss is 2.8847265
epoch time: 38912.93382644653, per step time: 84.96273761232868
epoch: 3 step: 458, loss is 2.8398118
epoch time: 38769.184827804565, per step time: 84.64887516987896
...

epoch: 498 step: 458, loss is 0.70908034
epoch time: 38771.079778671265, per step time: 84.65301261718616
epoch: 499 step: 458, loss is 0.7974688
epoch time: 38787.413120269775, per step time: 84.68867493508685
epoch: 500 step: 458, loss is 0.5548882
epoch time: 39064.8467540741, per step time: 85.29442522723602

Evaluation

for evaluation , run eval.py with checkpoint_path. checkpoint_path is the path of checkpoint file.

python eval.py --checkpoint_path ssd.ckpt --dataset coco

You can run python eval.py -h to get more information.

You will get the result as following:

Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.189
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.341
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.183
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.040
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.181
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.326
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.213
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.348
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.380
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.124
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.412
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.588

========================================

mAP: 0.18937438355383837