- implement a training script for NYUDv2 dataset (40 class challenge)
- deals with both RGB and RGB-D input images
- modify fcn8_vgg, fcn16_vgg, fcn32_vgg to handle dropout layer at test time using a tf.placeholder representing the dropout probability
This is a one file Tensorflow implementation of Fully Convolutional Networks in Tensorflow. The code can easily be integrated in your semantic segmentation pipeline. The network can be applied directly or finetuned to perform semantic segmentation using tensorflow training code.
Deconvolution Layers are initialized as bilinear upsampling. Conv and FCN layer weights using VGG weights. Numpy load is used to read VGG weights. No Caffe or Caffe-Tensorflow is required to run this. The .npy file for [VGG16] to be downloaded before using this needwork. You can find the file here: ftp://mi.eng.cam.ac.uk/pub/mttt2/models/vgg16.npy
No Pascal VOC finetuning was applied to the weights. The model is meant to be finetuned on your own data.
This code requires Tensorflow Version >= 1.0rc to run.
Tensorflow 1.0 comes with a large number of breaking api changes. If you are currently running an older tensorflow version, I would suggest creating a new virtualenv and install 1.2.
python train.py to start the training.
Currently the following Models are provided:
- FCN32
- FCN16
- FCN8
Weights were generated using Caffe to Tensorflow. The VGG implementation is based on tensorflow-vgg16 and numpy loading is based on tensorflow-vgg. You do not need any of the above cited code to run the model, not do you need caffe.
sudo apt-get install libpng-dev libjpeg8-dev libfreetype6-dev pkg-config
In addition to tensorflow the following packages are required:
numpy scipy pillow matplotlib
Those packages can be installed by running pip install -r requirements.txt or pip install numpy scipy pillow matplotlib.