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A tensorflow implementation for SqueezeDet, a convolutional neural network for object detection.
| Date | Stars |
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| 2026-07-31 | 741 |
| 2026-08-06 | 741 |
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## _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 tensorflow implementation of SqueezeDet, a convolutional neural network based object detector described in our paper: https://arxiv.org/abs/1612.01051. 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:
The following instructions are written for Linux-based distros.
- Clone the SqueezeDet repository:
```Shell
git clone https://github.com/BichenWuUCB/squeezeDet.git
```
Let's call the top level directory of SqueezeDet `$SQDT_ROOT`.
- (Optional) Setup your own virtual environment.
1. The following assumes `python` is the Python2.7 executable. Navigate to your user home directory, and create the virtual environment there.
```Shell
cd ~
virtualenv env --python=python
```
2. Launch the virtual environment.
```Shell
source env/bin/activate
```
- Use pip to install required Python packages:
```Shell
pip install -r requirements.txt
```
## Demo:
- Download SqueezeDet model parameters from [here](https://www.dropbox.com/s/a6t3er8f03gdl4z/model_checkpoints.tgz?dl=0), untar it, and put it under `$SQDT_ROOT/data/` If you are using command line, type:
```Shell
cd $SQDT_ROOT/data/
wget https://www.dropbox.com/s/a6t3er8f03gdl4z/model_checkpoints.tgz
tar -xzvf model_checkpoints.tgz
rm model_checkpoints.tgz
```
- Now we can run the demo. To detect the sample image `$SQDT_ROOT/data/sample.png`,
```Shell
cd $SQDT_ROOT/
python ./src/demo.py
```
If the installation is correct, the detector should generate this image: 
To detect other image(s), use the flag `--input_path=./data/*.png` to point to input image(s). Input image(s) will be scaled to the resolution of 1242x375 (KITTI image resolution), so it works best when original resolution is close to that.
- SqueezeDet is a real-time object detector, which can be used to detect videos. The video demo will be released later.
## Training/Validation:
- Download KITTI object detection dataset: [images](http://www.cvlibs.net/download.php?file=data_object_image_2.zip) and [labels](http://www.cvlibs.net/download.php?file=data_object_label_2.zip). Put them under `$SQDT_ROOT/data/KITTI/`. Unzip them, then you will get two directories: `$SQDT_ROOT/data/KITTI/training/` and `$SQDT_ROOT/data/KITTI/testing/`.
- Now we need to split the training data into a training set and a vlidation set.
```Shell
cd $SQDT_ROOT/data/KITTI/
mkdir ImageSets
cd ./ImageSets
ls ../training/image_2/ | grep ".png" | sed s/.png// > trainval.txt
```
`trainval.txt` contains 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. For your convenience, we provide a script to split the train-val set automatically. Simply run
```Shell
cd $SQDT_ROOT/data/
python random_split_train_val.py
```
then you should get the `train.txt` and `val.txt` under `$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_Excerpt of 6,844 characters
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matched fp:bb537075955e4f64, desc:object detection