This release includes updates to improve training and accuracy, and a new MS COCO trained model.

Remove unnecessary dropout layer
Reduce anchor stride from 2 to 1
Increase ROI training mini batch to 200 per image
Improve computing proposal positive:negative ratio
Updated COCO training schedule
Add --logs param to coco.py to set logging directory
Bug Fix: exclude BN weights from L2 regularization
Use mean (rather than sum) of L2 regularization for a smoother loss in TensorBoard
Better compatibility with Python 2.7
The new MS COCO trained weights improve the accuracy compared to the previous weights. These are the evaluation results on the minival dataset:

Evaluate annotation type bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.544
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.377
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.295
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.481
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
Evaluate annotation type segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.510
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.306
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.330
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.258
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.369
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538

This release includes updates to improve training and accuracy, and a new MS COCO trained model.

Remove unnecessary dropout layer
Reduce anchor stride from 2 to 1
Increase ROI training mini batch to 200 per image
Improve computing proposal positive:negative ratio
Updated COCO training schedule
Add --logs param to coco.py to set logging directory
Bug Fix: exclude BN weights from L2 regularization
Use mean (rather than sum) of L2 regularization for a smoother loss in TensorBoard
Better compatibility with Python 2.7
The new MS COCO trained weights improve the accuracy compared to the previous weights. These are the evaluation results on the minival dataset:

Evaluate annotation type bbox
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.347
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.544
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.377
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.163
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.390
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.486
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.295
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.424
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.433
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.214
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.481
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.601
Evaluate annotation type segm
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.296
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.510
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.306
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.128
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.330
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.430
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.258
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.369
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.376
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.173
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.417
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.538
Big thanks to everyone who contributed to this repo. Names are in the commits history.