code_test/configs/ssd/README.md

# SSD: Single Shot MultiBox Detector

## Introduction

<!-- [ALGORITHM] -->

```latex
@article{Liu_2016,
   title={SSD: Single Shot MultiBox Detector},
   journal={ECCV},
   author={Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.},
   year={2016},
}
```

## Results and models of SSD

| Backbone | Size  | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :------: | :---: | :---: | :-----: | :------: | :------------: | :----: | :------: |  :--------: |
|  VGG16   |  300  | caffe |  120e   |   9.9    |  43.7          |  25.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428-d231a06e.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428.log.json) |
|  VGG16   |  512  | caffe |  120e   |   19.4   |  30.7          |  29.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849.log.json) |

## Results and models of SSD-Lite

|    Backbone    | Size  | Training from scratch | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :------------: | :---: | :-------------------: | :-----: | :------: | :------------: | :----: | :------: |  :--------: |
|  MobileNetV2   |  320  |        yes            |  600e   |   4.0    |     69.9       |  21.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627.log.json) |

## Notice

### Compatibility

In v2.14.0, [PR5291](https://github.com/open-mmlab/mmdetection/pull/5291) refactored SSD neck and head for more
flexible usage. If users want to use the SSD checkpoint trained in the older versions, we provide a scripts
`tools/model_converters/upgrade_ssd_version.py` to convert the model weights.

```bash
python tools/model_converters/upgrade_ssd_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH}

```

- OLD_MODEL_PATH: the path to load the old version SSD model.
- NEW_MODEL_PATH: the path to save the converted model weights.

### SSD-Lite training settings

There are some differences between our implementation of MobileNetV2 SSD-Lite and the one in [TensorFlow 1.x detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md) .

1. Use 320x320 as input size instead of 300x300.
2. The anchor sizes are different.
3. The C4 feature map is taken from the last layer of stage 4 instead of the middle of the block.
4. The model in TensorFlow1.x is trained on coco 2014 and validated on coco minival2014, but we trained and validated the model on coco 2017. The mAP on val2017 is usually a little lower than minival2014 (refer to the results in TensorFlow Object Detection API, e.g., MobileNetV2 SSD gets 22 mAP on minival2014 but 20.2 mAP on val2017).