





Model Configuration
mAP delta
mAP




Baseline with "FasterRCNN-style" Hyperparams
-
19.5


+ Paper Hyperparams
1.6
21.1


+ Data Augmentation
1.8
22.9


+ Weight Initialization & Input Scaling
1
23.9


+ LR scheme
1.2
25.1



Our final model achieves an mAP of 25.1 and reproduces exactly the COCO results reported on the paper. Here is a detailed breakdown of the accuracy metrics.
We hope you found the part 1 of the series interesting. On the part 2, we will focus on the implementation of SSDlite and discuss its differences from SSD. Until then, we are looking forward to your feedback.