update docs

examples/FasterRCNN/README.md

 # Faster-RCNN / Mask-RCNN on COCO
-This example aims to provide a minimal (1.3k lines) implementation of
-end-to-end Faster-RCNN & Mask-RCNN (with ResNet & FPN backbones) on COCO.
+This example provides a minimal (only 1.6k lines) but faithful implementation the
+following papers in combination:
+
++ [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
++ [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144)
++ [Mask R-CNN](https://arxiv.org/abs/1703.06870)
 
 ## Dependencies
 + Python 3; TensorFlow >= 1.4.0 (>=1.6.0 recommended due to a TF bug);
@@ -53,18 +57,21 @@ MaskRCNN results contain both bbox and segm mAP.
 
 |Backbone|`FASTRCNN_BATCH`|resolution |schedule|mAP (bbox/segm)|Time          |
 |   -    |    -           |    -      |   -    |   -           |   -          |
-|R-50    |64              |(600, 1024)|280k    |33.1           |18h on 8 V100s|
-|R-50    |512             |(800, 1333)|280k    |35.6           |55h on 8 P100s|
-|R-50    |512             |(800, 1333)|360k    |36.6           |49h on 8 V100s|
-|R-50    |256             |(800, 1333)|280k    |36.8/32.1      |39h on 8 P100s|
-|R-50    |512							|(800, 1333)|360k    |37.8/33.2      |51h on 8 V100s|
-|R-101   |512             |(800, 1333)|280k    |40.1/34.4      |70h on 8 P100s|
-|R-101   |512             |(800, 1333)|360k    |40.8/35.1      |63h on 8 V100s|
+|R50-C4  |64              |(600, 1024)|280k    |33.1           |18h on 8 V100s|
+|R50-C4  |512             |(800, 1333)|280k    |35.6           |55h on 8 P100s|
+|R50-C4  |512             |(800, 1333)|360k    |36.6           |49h on 8 V100s|
+|R50-FPN |512             |(800, 1333)|360k    |37.5           |28h on 8 V100s|
+|R50-C4  |256             |(800, 1333)|280k    |36.8/32.1      |39h on 8 P100s|
+|R50-C4  |512							|(800, 1333)|360k    |37.8/33.2      |51h on 8 V100s|
+|R50-FPN |512							|(800, 1333)|360k    |38.1/34.9      |38h on 8 V100s|
+|R101-C4 |512             |(800, 1333)|280k    |40.1/34.4      |70h on 8 P100s|
+|R101-C4 |512             |(800, 1333)|360k    |40.8/35.1      |63h on 8 V100s|
 
 The two R-50 360k models have the same configuration __and mAP__
 as the `R50-C4-2x` entries in
 [Detectron Model Zoo](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#end-to-end-faster--mask-r-cnn-baselines).
-So far this seems to be the only open source re-implementation that can reproduce mAP in Detectron.
+So far this is the only TensorFlow implementation that can reproduce mAP in Detectron.
+The other models listed here do not correspond to any configurations in Detectron.
 
 ## Notes