Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/lyxok1/Tiny-DSOD
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usage
https://github.com/lyxok1/Tiny-DSOD
deep-learning lightweight object-detection
Last synced: 25 days ago
JSON representation
Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usage
- Host: GitHub
- URL: https://github.com/lyxok1/Tiny-DSOD
- Owner: lyxok1
- License: other
- Created: 2018-06-22T02:57:08.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-06-07T15:37:46.000Z (over 5 years ago)
- Last Synced: 2024-07-28T06:38:11.028Z (5 months ago)
- Topics: deep-learning, lightweight, object-detection
- Language: C++
- Size: 16.2 MB
- Stars: 231
- Watchers: 11
- Forks: 58
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AutoML-and-Lightweight-Models - lyxok1/Tiny-DSOD
README
# Tiny-DSOD: Lightweight Object Detection for Resource Restricted Usage
This repository releases the code for our paper
**Tiny-DSOD: Lightweight Object Detection for Resource Restricted Usage (BMVC2018)**
**Yuxi Li, [Jianguo Li](https://sites.google.com/site/leeplus/), Jiuwei Li and Weiyao Lin**
The code is based on the [SSD](https://github.com/weiliu89/caffe/tree/ssd) and [DSOD](https://github.com/szq0214/DSOD) framework.
## Introduction
Tiny-DSOD tries to tackle the trade-off between detection accuracy and computation resource consumption. In this work, our tiny-model outperforms other small sized detection network (pelee, mobilenet-ssd or tiny-yolo) in the metrics of FLOPs, parameter size and accuracy. To be specific, on the dataset of PASCAL VOC2007, Tiny-DSOD achieves mAP of 72.1% with less than 1 million parameters (0.95M)
## Preparation
1. Install dependencies our caffe framework needs. You can visit the [caffe official website](http://caffe.berkeleyvision.org/installation.html) and follow the instructions there to install the dependent libraries and drivers correctly.
2. Clone this repository and compile the code
```Shell
git clone https://github.com/lyxok1/Tiny-DSOD.git
cd Tiny-DSOD
# visit the Makefile then modify the compile options and path to your library there
make -j8
```
3. Prepare corresponding dataset (if need training). Please see the document in [SSD](https://github.com/weiliu89/caffe/tree/ssd) detail
## Train a model from scratch
Suppose the code is runing under the main directory of caffe.
First generate the model prototxt files
```Shell
python examples/DCOD/DCOD_pascal.py # for voc training
python examples/DCOD/DCOD_kitti.py # for kitti training
python examples/DCOD/DCOD_coco.py # for coco training
```
And then use the binary `./build/tools/caffe` to train the generated network
```Shell
./jobs/DCOD300/${DATASET}/DCOD300_300x300/DCOD300_${DATASET}_DCOD300_300x300.sh
# Alternatively, you can directly use the binary to train in command line
./build/tools/caffe train -solver models/DCOD300/$DATASET/DCOD300_300x300/solver.prototxt -gpu all 2>&1 | tee models/DCOD300/$DATASET/DCOD300_300x300/train.log
```
## Deploy a pre-trained model
If you want to directly deploy a pre-trained model, you can use the demo scripts we provide in the `example/DCOD/` directory
- for image input detection, use the following command:
```Shell
python examples/DCOD/image_detection_demo.py
optional arguments:
-h, --help show this help message and exit
-model MODEL path to model prototxt file
-weights WEIGHTS path to weight file
-img_dir IMG_DIR path to input image
-num NUM number of images for detection
-gpu specifiy using GPU or not
-threshold THRESHOLD threshold to filter bbox with low confidence
```
- for video input detection, use the following command:
```Shell
python examples/DCOD/video_detection_demo.py
optional arguments:
-h, --help show this help message and exit
-model MODEL path to model prototxt file
-weights WEIGHTS path to weight file
-video VIDEO path to input video
-gpu specifiy using GPU or not
-threshold THRESHOLD threshold to filter bbox with low confidence
```
## Results
- Results on PASCAL VOC2007 (the models are trained on VOC07+12 trainval and test on VOC07 test)
| Method | # Params | FLOPs | *mAP* |
|:-------|:--------:|:-----:|:-----:|
| Faster-RCNN | 134.70M | 181.12B | 73.2 |
| SSD | 26.30M | 31.75B | 77.2 |
| Tiny-YOLO | 15.12M | 6.97B | 57.1 |
| MobileNet-SSD | 5.50M | 1.14B | 68.0|
| DSOD-smallest | 5.90M | 5.29B | 73.6|
| Pelee | 5.98M | 1.21B | 70.9 |
| Tiny-DSOD | 0.95M | 1.06B | 72.1 |
- Results on KITTI 2D Object Detction (the models are trained on half KITTI trainval and test on the other half)
| Method | # Params | FLOPs | car | cyclist | pedestrain |*mAP* |
|:-------|:--------:|:-----:|:---:|:-------:|:----------:|:----:|
| MS-CNN | 80M | - | 85.0 | 75.2 | 75.3 | 78.5 |
| FRCN | 121.2M | - | 86.0 | - | - | - |
| SqueezeDet | 1.98M | 9.7B | 82.9 | 76.8 | 70.4 | 76.7 |
| Tiny-DSOD | 0.85M | 4.1B | 88.3 | 73.6 | 69.1 | 77.0 |
- Results on COCO (the models are trained on trainval 135k and test on test-dev 2015)
| Method | # Params | FLOPs | *mAP(IOU 0.5:0.95)* |
|:-------|:--------:|:-----:|:-----:|
|MobileNet-v2+SSDLite| 4.30M | 0.80B | 22.1 |
|Pelee | 5.98M | 1.29B | 22.4 |
|Yolo-v2 | 67.43M | 34.36B | 21.6 |
|Tiny-DSOD | 1.15M | 1.12B | 23.2 |
## Released model
We released a model pretrained on VOC2007 on [Baidu Yun (3.8MB)](https://pan.baidu.com/s/1tNEZRWHwoVSOIuYwz2DlVQ) and [Google Drive](https://drive.google.com/file/d/1G28_oKPWILZ1hGCJ1tfEFEx2obu0SeMK/view?usp=sharing)
## Example
## Citation
If you think this work is helpful for your own research, please consider add following bibtex config in your latex file
```Latex
@inproceedings{li2018tiny,
title = {{Tiny-DSOD}: Lightweight Object Detection for Resource-restricted Usage},
author = {Yuxi Li, Jianguo Li, Jiuwei Li and Weiyao Lin},
booktitle = {BMVC},
year = {2018}
}
```