update docs

examples/DoReFa-Net/README.md

@@ -37,7 +37,7 @@ We hosted a demo at CVPR16 on behalf of Megvii, Inc, running a 1/4-VGG size DoRe
 DoReFa-Net and its variants have been deployed widely in Megvii's embeded products.
 
 This code release is meant for research purpose. We're not planning to release our C++ runtime for bit-operations.
-In this implementation, quantized operations are all performed through `tf.float32`.
+In this implementation, quantized operations are all performed through `tf.float32`. They don't make your network faster.
 
 ## Use
 

examples/FasterRCNN/README.md

@@ -78,10 +78,12 @@ MaskRCNN results contain both box and mask mAP.
  | R50-FPN  | 37.4                                                                                                     | 37.9                                               | 30h             | <details><summary>standard</summary>`MODE_MASK=False MODE_FPN=True` </details>                                                                                                                                  |
  | R50-C4   | 37.8;33.1 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50C4-MaskRCNN-Standard.npz)       | 37.8;32.8                                          | 49h             | <details><summary>standard</summary>`MODE_MASK=True` </details>                                                                                                                                                 |
  | R50-FPN  | 38.2;34.9 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-Standard.npz)      | 38.6;34.5                                          | 32h             | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True` </details>                                                                                                                                   |
+ | R50-FPN  | 39.1;35.2                                                                                                | 38.6;34.5                                          | 32h             | <details><summary>better params</summary>`MODE_MASK=True MODE_FPN=True`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`FRCNN.BBOX_REG_WEIGHTS=[20,20,10,10]` </details>                                               |
  | R50-FPN  | 39.5;35.2                                                                                                | 39.5;34.4<sup>[2](#ft2)</sup>                      | 34h             | <details><summary>standard+ConvGNHead</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head` </details>                                                                    |
  | R50-FPN  | 40.0;36.1 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R50FPN-MaskRCNN-StandardGN.npz)    | 40.3;35.7                                          | 44h             | <details><summary>standard+GN</summary>`MODE_MASK=True MODE_FPN=True`<br/>`FPN.NORM=GN BACKBONE.NORM=GN`<br/>`FPN.FRCNN_HEAD_FUNC=fastrcnn_4conv1fc_gn_head`<br/>`FPN.MRCNN_HEAD_FUNC=maskrcnn_up4conv_gn_head` |
  | R101-C4  | 40.8;35.1 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101C4-MaskRCNN-Standard.npz)      |                                                    | 63h             | <details><summary>standard</summary>`MODE_MASK=True `<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details>                                                                                                     |
  | R101-FPN | 40.4;36.6 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101FPN-MaskRCNN-Standard.npz)     | 40.9;36.4                                          | 40h             | <details><summary>standard</summary>`MODE_MASK=True MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]` </details>                                                                                        |
+ | R101-FPN | 41.0;36.6 [:arrow_down:](http://models.tensorpack.com/FasterRCNN/COCO-R101FPN-MaskRCNN-BetterParams.npz) | 40.9;36.4                                          | 40h             | <details><summary>better params</summary>`MODE_MASK=True MODE_FPN=True`<br/>`BACKBONE.RESNET_NUM_BLOCK=[3,4,23,3]`<br/>`TEST.RESULT_SCORE_THRESH=1e-4`<br/>`FRCNN.BBOX_REG_WEIGHTS=[20,20,10,10]` </details>    |
 
  <a id="ft1">1</a>: Here we comapre models that have identical training & inference cost between the two implementation. However their numbers are different due to many small implementation details.