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https://github.com/alvinwan/squeezeDetMX
squeezeDet for object detection port to MXNet
https://github.com/alvinwan/squeezeDetMX
computer-vision deep-learning kitti-dataset mobile object-detection squeezedet
Last synced: about 2 months ago
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squeezeDet for object detection port to MXNet
- Host: GitHub
- URL: https://github.com/alvinwan/squeezeDetMX
- Owner: alvinwan
- License: bsd-2-clause
- Created: 2017-05-16T21:29:40.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2017-06-22T20:02:12.000Z (over 7 years ago)
- Last Synced: 2024-06-12T01:53:16.745Z (3 months ago)
- Topics: computer-vision, deep-learning, kitti-dataset, mobile, object-detection, squeezedet
- Language: Python
- Homepage:
- Size: 341 KB
- Stars: 19
- Watchers: 2
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-MXNet - SqueezeDet
README
# SqueezeDetMX
> This repository is built in Python3.6. With small syntax changes, it could be adapted to Python3.*. To my knowledge, those changes would render the repository backwards-compatible with Python2.*.
This repository contains an implementation of SqueezeDet, a ["unified, small, low power fully convolutional neural network for real-time object detection for autonomous driving"](https://arxiv.org/abs/1612.01051), in MXNet. Note that this *only* runs on Python3. It additionally holds utilities for reading and writing compact binary data, on top of deserialization functions for the KITTI dataset.
You can find the original squeezeDet implementation, using Tensorflow, [here](https://github.com/BichenWuUCB/squeezeDet). Parts of the codebase were taken from the original repository; all such instances have been cited accordingly.
# 1. Install
(Optional) We recommend setting up a virtual environment.
```
virtualenv squeezeDetMX --python=python3
source activate squeezeDetMX/bin/activate
```
Say `$SDMX_ROOT` is the root of your repository. Navigate to your root repository.
```
cd $SDMX_ROOT
```
We need to setup our Python dependencies.
```
pip install -r requirements.txt
```
# 2. Setup KITTI
*The first two steps were taken nearly word-for-word from the [original README](https://github.com/BichenWuUCB/squeezeDet#trainingvalidation).*
First, obtain the KITTI object detection dataset links: [images](http://www.cvlibs.net/download.php?file=data_object_image_2.zip) and [labels](http://www.cvlibs.net/download.php?file=data_object_label_2.zip). Start by creating and changing into a directory for KITTI.
```
mkdir $SDMX_ROOT/data/KITTI
cd $SDMX_ROOT/data/KITTI
```
Download and then unzip both files.
```
wget
unzip data_object_image_2.zip
wget
unzip data_object_label_2.zip
```
## Train-Val Splitting
Now we need to split the training data into a training set and a validation set. Create a directory to hold both.
```Shell
mkdir ImageSets
cd ./ImageSets
```
Then, create a new file `trainval.txt`, containing indices to all the images in the training data. In our experiments, we randomly split half of indices in `trainval.txt` into `train.txt` to form a training set and rest of them into `val.txt` to form a validation set.
```
ls ../training/image_2/ | grep ".png" | sed s/.png// > trainval.txt
```
For your convenience, we provide a script to split the train-val set automatically. Navigate to the data directory's root.
```
cd $SDMX_ROOT/data
```
Simply run
```Shell
python random_split_train_val.py
```
You should get both `train.txt` and `val.txt` under `$SQDT_ROOT/data/KITTI/ImageSets`.
When above two steps are finished, the structure of `$SQDT_ROOT/data/KITTI/` should at least contain:
```Shell
$SQDT_ROOT/data/KITTI/
|->training/
| |-> image_2/00****.png
| L-> label_2/00****.txt
|->testing/
| L-> image_2/00****.png
L->ImageSets/
|-> trainval.txt
|-> train.txt
L-> val.txt
```
## Converting into RecordIO
This repository additionally contains a conversion script, from KITTI to RecordIO file objects. Navigate to the repository root, and run `convert.py`.
```
cd $SDMX_ROOT
python convert.py
```
If your data was downloaded and setup per the above `Data` section, the script will require no flags. Otherwise, run `python convert.py --help` for more information.
# 3. Train
The repository will support any RecordIO binary, written using the `squeezeDetMX.utils.Writer` object. By default, the script will look for RecordIO objects in `./data/KITTI`. To start running, use
```
python train.py
```