SqueezeDet on Keras

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

By Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer (UC Berkeley & DeepScale)

This repository contains a Keras implementation of SqueezeDet, a convolutional neural network based object detector described in this paper: https://arxiv.org/abs/1612.01051. The original implementation can be found here. If you find this work useful for your research, please consider citing:

@inproceedings{squeezedet,
    Author = {Bichen Wu and Forrest Iandola and Peter H. Jin and Kurt Keutzer},
    Title = {SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving},
    Journal = {arXiv:1612.01051},
    Year = {2016}
}

Installation

Please have a look at our Installation Guide

How do I run it?

I will show an example on the KITTI dataset. If you have any doubts, most scripts run with the -h flag give you the arguments you can pass

Download the KITTI training example from here and here
Unzip them

unzip data_object_image_2.zip

unzip data_object_label_2.zip

You should get a folder called training.
Inside the repository folder create a folder for the experiment. If you don't mind or dont want to type .. all the time you can do it in the scripts folder

cd path/to/squeezeDet

mkdir experiments

mkdir experiments/kitti

cd experiments/kitti
SqueezeDet takes a list of images with full paths to the images and the same for labels. It's the same for training and evaluation. Create a list of full path names of images and labels:

find /path/to/training/image_2/ -name "*png" | sort > images.txt

find /path/to/training/label_2/ -name "*txt" | sort > labels.txt
Create a training test split

python ../../main/utils/train_val_split.py

You should get img_train.txt, gt_train.txt, img_val.txt gt_val.txt, img_test.txt, gt_test.txt . Testing set is empty by default.
Create a config file

python ../../main/config/create_config.py

Depending on the GPU change the batch size inside squeeze.config and other parameters like learning rate.
Run training, this starts with pre-trained weights from imagenet

python ../../scripts/train.py --init ../../main/model/imagenet.h5
In another shell, to run evaluation
- If you have no second GPU or none at all:
  
  python ../../scripts/eval.py --gpu ""
- Otherwise:
  
  python ../../scripts/eval.py
  
  This will run evaluation in parallel on the second GPU.
To run training on multiple GPUS:

python ../../scripts/train.py --gpus 2 --init ../../main/model/imagenet.h5

To run on the first 2 GPUS. Then you have to run evaluation on the third or CPU, if you have it.
scripts/scheduler.py allows you to run multiple trainings after another. Check out the dummy scripts/schedule.config for an example. Run this with

python ../../scripts/scheduler.py --schedule ../../scripts/schedule.config --train ../../scripts/train.py --eval ../../scripts/eval.py

Tensorboard visualization

For tensoboard visualization you can can run:

tensorboard --logdir log

Open in your brower localhost:6006 or the IP where you ran the training. On the first page you can see the losses, sublosses and metrics like mean average precision and f1 scores.

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On the second page, you find visualizations of a couple of validation images with their ground truth bounding boxes and how the predictions change over the course of the training.

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The third page gives you a nice view over the network graph.

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