Direct model links to models.tensorpack.com

examples/ConvolutionalPoseMachines/README.md

@@ -16,7 +16,7 @@ wget https://github.com/shihenw/convolutional-pose-machines-release/raw/master/m
 python -m tensorpack.utils.loadcaffe pose_deploy_resize.prototxt pose_iter_320000.caffemodel CPM-original.npy
 ```
 
-Or you can download the converted model from [model zoo](https://drive.google.com/open?id=0B9IPQTvr2BBkRU8zM2w2ZGh3eU0).
+Or you can download the converted model from [model zoo](http://models.tensorpack.com/caffe/).
 
 Run it on an image, and produce `output.jpg`:
 ```

examples/DeepQNetwork/README.md

@@ -46,6 +46,6 @@ Watch the agent play:
 ```
 ./DQN.py --rom breakout.bin --task play --load path/to/model
 ```
-A pretrained model on breakout can be downloaded [here](https://drive.google.com/open?id=0B9IPQTvr2BBkN1Jrei1xWW0yR28).
+A pretrained model on breakout can be downloaded [here](http://models.tensorpack.com/DeepQNetwork/).
 
 A3C code and models for Atari games in OpenAI Gym are released in [examples/A3C-Gym](../A3C-Gym)

examples/DoReFa-Net/README.md

@@ -7,7 +7,7 @@ We're not planning to release our C++ runtime for bit-operations.
 In this repo, bit operations are performed through `tf.float32`.
 
 Pretrained model for (1,4,32)-ResNet18 and (1,2,6)-AlexNet are available at
-[google drive](https://drive.google.com/a/megvii.com/folderview?id=0B308TeQzmFDLa0xOeVQwcXg1ZjQ).
+[tensorpack model zoo](http://models.tensorpack.com/DoReFa-Net/).
 They're provided in the format of numpy dictionary, so it should be very easy to port into other applications.
 The __binary-weight 4-bit-activation ResNet-18__ model has 59.2% top-1 validation accuracy.
 

examples/FasterRCNN/README.md

@@ -37,7 +37,7 @@ To predict on an image (and show output in a window):
 ./train.py --predict input.jpg --load /path/to/model
 ```
 
-To evaluate the performance (pretrained models can be downloaded in [model zoo](https://drive.google.com/open?id=1J0xuDAuyOWiuJRm2LfGoz5PUv9_dKuxq):
+To evaluate the performance (pretrained models can be downloaded in [model zoo](http://models.tensorpack.com/FasterRCNN):
 ```
 ./train.py --evaluate output.json --load /path/to/model
 ```

examples/GAN/BEGAN.py

@@ -19,8 +19,7 @@ from GAN import GANModelDesc, GANTrainer, MultiGPUGANTrainer
 Boundary Equilibrium GAN.
 See the docstring in DCGAN.py for usage.
 
-A pretrained model on CelebA is at
-https://drive.google.com/open?id=0B5uDfUQ1JTglUmgyZV8zQmNOTVU
+A pretrained model on CelebA is at http://models.tensorpack.com/GAN/
 """
 
 

examples/GAN/ConditionalGAN-mnist.py

@@ -24,7 +24,7 @@ To train:
 To visualize:
     ./ConditionalGAN-mnist.py --sample --load path/to/model
 
-A pretrained model is at https://drive.google.com/open?id=0B9IPQTvr2BBkLUF2M0RXU1NYSkE
+A pretrained model is at http://models.tensorpack.com/GAN/
 """
 
 BATCH = 128

examples/GAN/DCGAN.py

@@ -31,7 +31,7 @@ from GAN import GANTrainer, RandomZData, GANModelDesc
 You can also train on other images (just use any directory of jpg files in
 `--data`). But you may need to change the preprocessing.
 
-A pretrained model on CelebA is at https://drive.google.com/open?id=0B9IPQTvr2BBkLUF2M0RXU1NYSkE
+A pretrained model on CelebA is at http://models.tensorpack.com/GAN/
 """
 
 # global vars

examples/GAN/InfoGAN-mnist.py

@@ -25,7 +25,7 @@ To train:
 To visualize:
     ./InfoGAN-mnist.py --sample --load path/to/model
 
-A pretrained model is at https://drive.google.com/open?id=0B9IPQTvr2BBkLUF2M0RXU1NYSkE
+A pretrained model is at http://models.tensorpack.com/GAN/
 """
 
 BATCH = 128

examples/HED/README.md

@@ -33,7 +33,7 @@ To inference (produce a heatmap at each level at out*.png):
 ```bash
 ./hed.py --load pretrained.model --run a.jpg
 ```
-Models I trained can be downloaded [here](https://drive.google.com/drive/folders/0B5uDfUQ1JTgldzVLaDBERG9zQmc?usp=sharing).
+Models I trained can be downloaded [here](http://models.tensorpack.com/HED/).
 
 To view the loss curve:
 ```bash

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://drive.google.com/open?id=0B9IPQTvr2BBkTXBlZmh1cmlnQ0k) and [here](https://drive.google.com/open?id=0B9IPQTvr2BBkQk9qcmtGSERlNUk).
+[here](https://goo.gl/6XjK9V) and [here](http://models.tensorpack.com/Visualization/).
 
 2. Generate CAM on ImageNet validation set:
 ```bash

examples/ShuffleNet/README.md

@@ -22,7 +22,7 @@ Train (takes 24 hours on 8 Maxwell TitanX):
 ./shufflenet.py --data /path/to/ilsvrc/
 ```
 
-Eval the [pretrained model](https://drive.google.com/open?id=1Q1C2BCkbOK2HfhUB0Yfn_W_F68bqnA6y):
+Eval the [pretrained model](http://models.tensorpack.com/ShuffleNet/):
 ```
 ./shufflenet.py --eval --data /path/to/ilsvrc --load /path/to/model
 ```

examples/SpatialTransformer/README.md

@@ -20,7 +20,7 @@ To train (takes about 300 epochs to reach 8.8% error):
 ./mnist-addition.py
 ```
 
-To draw the above visualization with [pretrained model](https://drive.google.com/drive/folders/0B9IPQTvr2BBkUWM3X0hDZHJtTmc?usp=sharing):
+To draw the above visualization with [pretrained model](http://models.tensorpack.com/SpatialTransformer/):
 ```bash
 ./mnist-addition.py --load pretrained.npy --view
 ```

examples/load-vgg16.py

@@ -26,6 +26,8 @@ Usage:
             PATH/TO/VGG/{VGG_ILSVRC_16_layers_deploy.prototxt,VGG_ILSVRC_16_layers.caffemodel} vgg16.npy
 
     Or download a converted caffe model from http://models.tensorpack.com/caffe/
+
+    Then, run it:
     ./load-vgg16.py --load vgg16.npy --input cat.png
 """