MobileNet_V3_SSD.pytorch

Install

Install PyTorch 1.0 are command.
Clone this repository.(Note: We currently only support Python 3+.)
git clone https://github.com/chuliuT/MobileNet_V3_SSD.pytorch.git

Examples

Dataset

To make things easy, we provide bash scripts to handle the dataset downloads and setup for you. We also provide simple dataset loaders that inherit torch.utils.data.Dataset, making them fully compatible with the torchvision.datasets API.

COCO

Microsoft COCO: Common Objects in Context

Download COCO 2014

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh

VOC Dataset

PASCAL VOC: Visual Object Classes

Download VOC2007 trainval & test

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>

Download VOC2012 trainval

# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

Training SSD

To train SSD using the train script simply specify the parameters listed in train.py as a flag or manually change them.

python3 train.py

Evaluation

To evaluate a trained network:

python3 eval.py

Performance

VOC2007 Test

mAP

Original	Converted weiliu89 weights	From scratch w/o data aug	From scratch w/ data aug
77.2 %	None	None	49.26%

FPS(there have some unknown bugs)

cpu:5 fps

Demos

cd demo
python3 demo.py

Camera

cd demo
python3 live.py

Note: Unfortunately, Mean-ap is too low,the fps measurement is worst

Code References：

https://github.com/kuan-wang/pytorch-mobilenet-v3

https://github.com/songwsx/steel-detect

https://github.com/amdegroot/ssd.pytorch

-- update date 2019.12.09

add modified training code

-- update date 2019.12.11

fix a model bug in test mode

VOC07 metric? Yes AP for aeroplane = 0.6109 AP for bicycle = 0.5853 AP for bird = 0.3183 AP for boat = 0.3695 AP for bottle = 0.1458 AP for bus = 0.6241 AP for car = 0.6528 AP for cat = 0.5913 AP for chair = 0.2562 AP for cow = 0.4363 AP for diningtable = 0.5608 AP for dog = 0.5212 AP for horse = 0.6694 AP for motorbike = 0.6020 AP for person = 0.5571 AP for pottedplant = 0.1620 AP for sheep = 0.4627 AP for sofa = 0.5607 AP for train = 0.6718 AP for tvmonitor = 0.4945 Mean AP = 0.4926

Results computed with the unofficial Python eval code.

Results should be very close to the official MATLAB eval code.

-- update date 2019.12.12

chuliuT/MobileNet_V3_SSD.pytorch