Awesome

YOLO-v2: Real-Time Object Detection

Still under development. 71 mAP(darknet) and 74mAP(resnet50) on VOC2007 achieved so far.

This is a pre-released version.

What's new

This repo is now deprecated, I am migrating to the latest Gluon-CV which is more user friendly and has a lot more algorithms in development.

Pretrained YOLOv3 models which achiveve 81%+ mAP on VOC and near 37% mAP on COCO: Model Zoo.
Object Detection model tutorials.

This repo will not receive active development, however, you can continue use it with the mxnet 1.1.0(probably 1.2.0).

Disclaimer

This is a re-implementation of original yolo v2 which is based on darknet. The arXiv paper is available here.

Demo

demo1

Getting started

Build from source, this is required because this example is not merged, some custom operators are not presented in official MXNet. Instructions
Install required packages: cv2, matplotlib

Try the demo

Download the pretrained model(darknet as backbone), or this model(resnet50 as backbone) and extract to model/ directory.
Run

# cd /path/to/mxnet-yolo
python demo.py --cpu
# available options
python demo.py -h

Train the model

Grab a pretrained model, e.g. darknet19
(optional) Grab a pretrained resnet50 model, resnet-50-0000.params,resnet-50-symbol.json, this will produce slightly better mAP than darknet in my experiments.
Download PASCAL VOC dataset.

cd /path/to/where_you_store_datasets/
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
# Extract the data.
tar -xvf VOCtrainval_11-May-2012.tar
tar -xvf VOCtrainval_06-Nov-2007.tar
tar -xvf VOCtest_06-Nov-2007.tar
ln -s /path/to/VOCdevkit /path/to/mxnet-yolo/data/VOCdevkit

Create packed binary file for faster training

# cd /path/to/mxnet-ssd
bash tools/prepare_pascal.sh
# or if you are using windows
python tools/prepare_dataset.py --dataset pascal --year 2007,2012 --set trainval --target ./data/train.lst
python tools/prepare_dataset.py --dataset pascal --year 2007 --set test --target ./data/val.lst --shuffle False

Start training

python train.py --gpus 0,1,2,3 --epoch 0
# choose different networks, such as resnet50_yolo
python train.py --gpus 0,1,2,3 --network resnet50_yolo --data-shape 416 --pretrained model/resnet-50 --epoch 0
# see advanced arguments for training
python train.py -h