VGG16 model for PaddlePaddle

The model is converted from Caffe model VGG_ILSVRC_16_layers, using caffe2fluid.

Usage

1. Clone this repo

   git clone https://github.com/oraoto/paddle-vgg16.git

   or simply download the model definition: vgg16.py

2. Download the params

   • Google Drive

3. Extract the params to models/vgg16, the resulting folder structure should looks like:

   .
   ├── vgg16.py
   ├── infer.py
   ├── models
   │   ├── vgg16
   │   │   ├── conv1_1_biases
   │   │   ├── conv1_1_weights
   │   │   ├── conv1_2_biases
   │   │   │....
   
   

4. Load params (see infer.py for full example)

   from vgg16 import VGG16
   import paddle.fluid as fluid
   
   img = fluid.layers.data('img', shape=[3, 224, 224])
   predict = VGG16(include_top=True, infer=True).net(img)
   
   exe = fluid.Executor(fluid.CPUPlace())
   # All params are prefiexed with `vgg16` to avoid confliction,
   # load params from `models` rather than `models/vgg16`
   fluid.io.load_params(exe, "models") 

5. Optional: Do inference on an image:

   python infer.py path/to/your/image
   

   Example output:

   $ python infer.py images/zebra_wikipedia.jpg
   Using TensorFlow backend.
   zebra: 0.999809205532
   impala: 0.000101726756839
   hartebeest: 4.52961321571e-05
   gazelle: 3.82991747756e-05
   ostrich: 3.12061411023e-06