The KITTI Vision Benchmark Suite

ResNet-101 Fully Convolutional Network with 4
upsampling steps with bilinear kernels and 3
skip connections. Model pretrained on ImageNet
and finetuned on the images of KITTI Road
training set in Bird's Eye View, with random
data augmentation recipes performed on training
time. These recipes include geometric
transformations (affine and perspective
transformations, mirroring, cropping and
distortion) and pixel-value changes (noise,
blur, color-space changes).

The model runs on GPU using Caffe deep learning
framework. Its Python interface is used to
define prototypes and control training process.
Also a custom data layer written in Python is
used to preprocess the images and its labels,
perform data augmentation and pass them to the
network during training. This code runs on a
single CPU core.

Stochastic Gradient Descent (SGD)
lr = 5e-5
wd = 5e-4
batch size = 1
momentum = 0.99
iterations = 20k

@inproceedings{munoz-bulnes_deep_2017,
author = {Muñoz-Bulnes, Jesús and
Fernandez, Carlos and Parra, Ignacio and
Fernández-Llorca, David and Sotelo, Miguel A.},
title = {Deep {Fully} {Convolutional}
{Networks} with {Random} {Data} {Augmentation}
for {Enhanced} {Generalization} in {Road}
{Detection}},
booktitle = {Workshop on {Deep}
{Learning} for {Autonomous} {Driving} on {IEEE}
20th {International} {Conference} on
{Intelligent} {Transportation} {Systems}},
address = {Yokohama, Japan},
month = oct,
year = {2017},
}