Releases: matterport/Mask_RCNN
Releases · matterport/Mask_RCNN
Mask R-CNN 2.1
This release adds:
- The Balloon Color Splash sample, along with dataset and trained weights.
- Convert the last prediction layer from Python to TensorFlow operations.
- Automatic download of COCO weights and dataset.
- Fixes for running on Windows.
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.
Mask R-CNN 2.0
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.
Mask R-CNN 1.0
v1.0 First release. v1.0.