Break down of key accuracy improvements
As discussed in previous articles, reproducing research papers and porting them to code is not a journey of monotonically increasing accuracies, especially in cases where the full training and implementation details are not known. Typically the process involves lots of backtracking as one needs to identify those implementation details and parameters that have significant impact on the accuracy from those that don’t. Below we try to visualize the most important iterations that improved our accuracy from the baseline:



{:.table.table-striped.table-bordered}
| Iteration | mAP | 
| ------------- | ------------- |
| Baseline with "SSD-style" Hyperparams | 10.6 | 
| + Tuned Hyperparams | 14.2 | 
| + SSDlite Data Augmentation | 15.2 |
| + 3x LR Scheme | 19.4 |
| + 6x LR Scheme | 20.1 |