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89 lines
3.2 KiB
89 lines
3.2 KiB
5 years ago
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# SSD Example
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## Description
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SSD network based on MobileNetV2, with support for training and evaluation.
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Dataset
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We use coco2017 as training dataset in this example by default, and you can also use your own datasets.
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1. If coco dataset is used. **Select dataset to coco when run script.**
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Download coco2017: [train2017](http://images.cocodataset.org/zips/train2017.zip), [val2017](http://images.cocodataset.org/zips/val2017.zip), [test2017](http://images.cocodataset.org/zips/test2017.zip), [annotations](http://images.cocodataset.org/annotations/annotations_trainval2017.zip). Install pycocotool.
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```
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pip install Cython
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pip install pycocotools
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```
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And change the COCO_ROOT and other settings you need in `config.py`. The directory structure is as follows:
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```
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└─coco2017
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├── annotations # annotation jsons
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├── train2017 # train dataset
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└── val2017 # infer dataset
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```
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2. If your own dataset is used. **Select dataset to other when run script.**
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Organize the dataset infomation into a TXT file, each row in the file is as follows:
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```
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train2017/0000001.jpg 0,259,401,459,7 35,28,324,201,2 0,30,59,80,2
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```
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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`.
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## Running the example
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### Training
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To train the model, run `train.py`. If the `MINDRECORD_DIR` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/en/master/use/data_preparation/converting_datasets.html) files by `COCO_ROOT`(coco dataset) or `IMAGE_DIR` and `ANNO_PATH`(own dataset). **Note if MINDRECORD_DIR isn't empty, it will use MINDRECORD_DIR instead of raw images.**
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- Stand alone mode
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```
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python train.py --dataset coco
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```
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You can run ```python train.py -h``` to get more information.
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- Distribute mode
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```
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sh run_distribute_train.sh 8 150 coco /data/hccl.json
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```
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The input parameters are device numbers, epoch size, dataset mode and [hccl json configuration file](https://www.mindspore.cn/tutorial/en/master/advanced_use/distributed_training.html). **It is better to use absolute path.**
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You will get the loss value of each step as following:
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```
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epoch: 1 step: 455, loss is 5.8653416
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epoch: 2 step: 455, loss is 5.4292373
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epoch: 3 step: 455, loss is 5.458992
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...
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epoch: 148 step: 455, loss is 1.8340507
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epoch: 149 step: 455, loss is 2.0876894
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epoch: 150 step: 455, loss is 2.239692
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```
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### Evaluation
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for evaluation , run `eval.py` with `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/en/master/use/saving_and_loading_model_parameters.html) file.
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```
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python eval.py --ckpt_path ssd.ckpt --dataset coco
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```
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You can run ```python eval.py -h``` to get more information.
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