Update README.md

README.md

@@ -14,18 +14,43 @@ The Problem With MaskRCNN (and Bounding Boxes)
 Due to bounding box ambiguity, **Mask R-CNN** fails in relatively dense scenes with objects of the same class, particularly if those objects have high bounding box overlap. In these scenes, **both recall (due to NMS) and precision (foreground instance class ambiguity)** are affected.
 ![alt text](demo/mrcnn_pencils.png)
 
+MaskRCNN takes a bounding box input to output a single foreground (instance) segmentation per class. The hidden assumption here (as is common in many detection networks) is that a good bounding box contains just one object in that class. This is not the case for dense scenes like the pencil image above.
+
+Unfortunately, such scenes are underrepresented in the most popular instance segmentation datasets - MSCOCO, Pascal VOC, Cityscapes. Yet they are not uncommon in many real-world applications e.g. robotics/logistics, household objects i.e. pens/chopsticks, etc. As a result, I've released **a simple, small dataset called PPC - Pens, Pencils, Chopsticks (see below)**, and show the significant difference between Mask R-CNN and Rotated Mask R-CNN in such scenes.
+
 Rotated Mask R-CNN
 -------------
 ![alt text](demo/rotated_mrcnn_pencils.png)
 
 **Rotated Mask R-CNN** resolves some of these issues by adopting a rotated bounding box representation.
 
-[Rotated Mask R-CNN]() extends Faster R-CNN, Mask R-CNN, or even RPN-only to work with rotated bounding boxes.
+This repository extends Faster R-CNN, Mask R-CNN, or even RPN-only to work with rotated bounding boxes.
 
 This work also builds on the Mask Scoring R-CNN ('MS R-CNN') paper by learning the quality of the predicted instance masks ([maskscoring_rcnn](https://github.com/zjhuang22/maskscoring_rcnn)).
 
 The repo master branch is fully merged upstream with the latest master branch of [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) (as of 25/07/2019)
 
+Results
+------------  
+**COCO**  
+Trained on coco/train2014, evaluated on coco/val2014
+
+| Backbone  | Method | mAP(mask) |
+|----------|--------|-----------|
+| ResNet-50 FPN | Mask R-CNN | 34.1 |
+| ResNet-50 FPN | MS R-CNN | 35.3 |
+| ResNet-50 FPN | Rotated Mask R-CNN | 33.4 |
+| ResNet-50 FPN | Rotated MS R-CNN | 34.7 |
+
+**PPC (Pens, Pencils, Chopsticks)**
+PPC (Pens, Pencils, Chopsticks) dataset: [Link](https://drive.google.com/open?id=1B4jV49KGVJtiZJVxcG11kNzGQKV02NHz)  
+Trained on train.json, evaluated on test.json (pens & pencils only, no chopstick class)
+
+| Backbone  | Method | mAP(mask) |
+|----------|--------|-----------|
+| ResNet-50 FPN | MS R-CNN | 13.2 |
+| ResNet-50 FPN | Rotated MS R-CNN | 19.3 |
+
 Additional Features
 -----------------
 - Soft NMS (Implemented for both bounding box and rotated detections. [Original repo](https://github.com/bharatsingh430/soft-nms))
@@ -103,28 +128,6 @@ Visualizing Rotated RPN Anchors
 ```
 Can be a useful tool for visualizing base Rotated RPN anchors. Use it to adjust the anchor sizes and ratios (and angles, if needed) for your application. 
 
-Results
-------------  
-**COCO**  
-Trained on coco/train2014, evaluated on coco/val2014
-
-| Backbone  | Method | mAP(mask) |
-|----------|--------|-----------|
-| ResNet-50 FPN | Mask R-CNN | 34.1 |
-| ResNet-50 FPN | MS R-CNN | 35.3 |
-| ResNet-50 FPN | Rotated Mask R-CNN | 33.4 |
-| ResNet-50 FPN | Rotated MS R-CNN | 34.7 |
-
-**PPC**  
-PPC (pens, pencils, chopsticks) dataset: [Link](https://drive.google.com/open?id=1B4jV49KGVJtiZJVxcG11kNzGQKV02NHz)  
-Trained on train.json, evaluated on test.json (pens & pencils only, no chopstick class)
-
-| Backbone  | Method | mAP(mask) |
-|----------|--------|-----------|
-| ResNet-50 FPN | MS R-CNN | 13.2 |
-| ResNet-50 FPN | Rotated MS R-CNN | 19.3 |
-
-
 Other Examples
 -------------
 ![alt text](demo/rotated_people_skateboard.png)