update models.

Author: Xinlei Chen

README.md

@@ -14,11 +14,11 @@ With VGG16 (``conv5_3``):
 With Resnet101 (last ``conv4``):
   - Train on VOC 2007 trainval and test on VOC 2007 test, **75.7**.
   - Train on VOC 2007+2012 trainval and test on VOC 2007 test (R-FCN schedule), **79.8**.
-  - Train on COCO 2014 trainval35k and test on minival (900k/1190k), **35.2**.
+  - Train on COCO 2014 trainval35k and test on minival (900k/1190k), **35.4**.
 
 More Results:
   - Train Mobilenet (1.0, 224) on COCO 2014 trainval35k and test on minival (900k/1190k), **21.8**.
-  - Train Resnet50 on COCO 2014 trainval35k and test on minival (900k/1190k), **32.3**.
+  - Train Resnet50 on COCO 2014 trainval35k and test on minival (900k/1190k), **32.4**.
   - Train Resnet152 on COCO 2014 trainval35k and test on minival (900k/1190k), **36.1**.
 
 Approximate *baseline* [setup](https://github.com/endernewton/tf-faster-rcnn/blob/master/experiments/cfgs/res101-lg.yml) from [FPN](https://arxiv.org/abs/1612.03144) (this repo does not contain training code for FPN yet):
@@ -37,7 +37,7 @@ Approximate *baseline* [setup](https://github.com/endernewton/tf-faster-rcnn/blo
   - For Resnets, we fix the first block (total 4) when fine-tuning the network, and only use ``crop_and_resize`` to resize the RoIs (7x7) without max-pool (which I find useless especially for COCO). The final feature maps are average-pooled for classification and regression. All batch normalization parameters are fixed. Learning rate for biases is not doubled.
   - For Mobilenets, we fix the first five layers when fine-tuning the network. All batch normalization parameters are fixed. Weight decay for Mobilenet layers is set to 4e-5.
   - For approximate [FPN](https://arxiv.org/abs/1612.03144) baseline setup we simply resize the image with 800 pixels, add 32^2 anchors, and take 1000 proposals during testing.
-  - Check out [here](http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](http://xinlei.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ) for the latest models **Needs to be updated**, including longer COCO VGG16 models and Resnet ones.
+  - Check out [here](http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](http://xinlei.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ) for the latest models, including longer COCO VGG16 models and Resnet ones.
 
 ### Additional features
 Additional features not mentioned in the [report](https://arxiv.org/pdf/1702.02138.pdf) are added to make research life easier: