- [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage)
# [SSD Description](#contents)
SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape.Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
[Paper](https://arxiv.org/abs/1512.02325): Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg.European Conference on Computer Vision (ECCV), 2016 (In press).
The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes, followed by a non-maximum suppression step to produce the final detections. The early network layers are based on a standard architecture used for high quality image classification, which is called the base network. Then add auxiliary structure to the network to produce detections.
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.
2. If VOC dataset is used. **Select dataset to voc when run script.**
Change `classes`, `num_classes`, `voc_json` and `voc_root` in `src/config.py`. `voc_json` is the path of json file with coco format for evalution, `voc_root` is the path of VOC dataset, the directory structure is as follows:
```
.
└─voc_dataset
└─train
├─0001.jpg
└─0001.xml
...
├─xxxx.jpg
└─xxxx.xml
└─eval
├─0001.jpg
└─0001.xml
...
├─xxxx.jpg
└─xxxx.xml
```
3. If your own dataset is used. **Select dataset to other when run script.**
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 `src/config.py`.
"save_checkpoint_epochs": 10 # The epoch interval between two checkpoints. By default, the checkpoint will be saved per 10 epochs
"loss_scale": 1024 # Loss scale
"filter_weight": False # Load paramters in head layer or not. If the class numbers of train dataset is different from the class numbers in pre_trained checkpoint, please set True.
"freeze_layer": "none" # Freeze the backbone paramters or not, support none and backbone.
"class_num": 81 # Dataset class number
"image_shape": [300, 300] # Image height and width used as input to the model
To train the model, run `train.py`. If the `mindrecord_dir` is empty, it will generate [mindrecord](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/convert_dataset.html) files by `coco_root`(coco dataset), `voc_root`(voc 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.**
sh run_distribute_train.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [RANK_TABLE_FILE] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
```
We need five or seven parameters for this scripts.
-`DEVICE_NUM`: the device number for distributed train.
-`EPOCH_NUM`: epoch num for distributed train.
-`LR`: learning rate init value for distributed train.
-`DATASET`：the dataset mode for distributed train.
-`RANK_TABLE_FILE :` the path of [rank_table.json](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools), it is better to use absolute path.
-`PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
-`PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
Training result will be stored in the current path, whose folder name begins with "LOG". Under this, you can find checkpoint file together with result like the followings in log
sh run_distribute_train_gpu.sh [DEVICE_NUM] [EPOCH_SIZE] [LR] [DATASET] [PRE_TRAINED](optional) [PRE_TRAINED_EPOCH_SIZE](optional)
```
We need five or seven parameters for this scripts.
-`DEVICE_NUM`: the device number for distributed train.
-`EPOCH_NUM`: epoch num for distributed train.
-`LR`: learning rate init value for distributed train.
-`DATASET`：the dataset mode for distributed train.
-`PRE_TRAINED :` the path of pretrained checkpoint file, it is better to use absolute path.
-`PRE_TRAINED_EPOCH_SIZE :` the epoch num of pretrained.
Training result will be stored in the current path, whose folder name is "LOG". Under this, you can find checkpoint files together with result like the followings in log
