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README.md

SSD: Single Shot MultiBox Detector

Introduction

@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 model | log
VGG16 512 caffe 120e 19.4 30.7 29.5 config model | log

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 model | log

Notice

Compatibility

In v2.14.0, PR5291 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.

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 .

  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).