Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
This release adds:
Thanks to everyone who made this possible with fixes and pull requests.
Note: COCO weights are not updated in this release. Continue to use the .h5 file from release 2.0.
This release includes updates to improve training and accuracy, and a new MS COCO trained model.
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.