use links in model zoo

examples/A3C-Gym/README.md

@@ -31,7 +31,7 @@ multiprocess Python program to get a cgroup dedicated for the task.
 
 ### To test a model:
 
-Download models from [model zoo](https://goo.gl/9yIol2).
+Download models from [model zoo](http://models.tensorpack.com/OpenAIGym/).
 
 Watch the agent play:
 `./train-atari.py --task play --env Breakout-v0 --load Breakout-v0.npz`

examples/FasterRCNN/README.md

@@ -5,7 +5,7 @@ Faster-RCNN / Mask-RCNN (without FPN) on COCO.
 ## Dependencies
 + Python 3; TensorFlow >= 1.4.0
 + [pycocotools](https://github.com/pdollar/coco/tree/master/PythonAPI/pycocotools), OpenCV.
-+ Pre-trained [ResNet model](https://goo.gl/6XjK9V) from tensorpack model zoo.
++ Pre-trained [ResNet model](http://models.tensorpack.com/ResNet/) from tensorpack model zoo.
 + COCO data. It assumes the following directory structure:
 ```
 DIR/

examples/ResNet/README.md

@@ -9,7 +9,7 @@ __Training__ code of three variants of ResNet on ImageNet:
 
 The training mostly follows the setup in [fb.resnet.torch](https://github.com/facebook/fb.resnet.torch)
 and gets similar performance (with much fewer lines of code).
-Models can be [downloaded here](https://goo.gl/6XjK9V).
+Models can be [downloaded here](http://models.tensorpack.com/ResNet/).
 
 | Model              | Top 5 Error | Top 1 Error |
 |:-------------------|-------------|------------:|

examples/Saliency/README.md

@@ -39,7 +39,7 @@ Usage:
 ./CAM-resnet.py --data /path/to/imagenet [--load ImageNet-ResNet18-Preact.npz] [--gpu 0,1,2,3]
 ```
 Pretrained and fine-tuned ResNet can be downloaded
-[here](https://goo.gl/6XjK9V) and [here](http://models.tensorpack.com/Visualization/).
+[here](http://models.tensorpack.com/ResNet/) and [here](http://models.tensorpack.com/Visualization/).
 
 2. Generate CAM on ImageNet validation set:
 ```bash