YOLO Caffe version with MaskRCNN

Statement: This repo was done before YOLACT: Real-time Instance Segmentation, ICCV 2019. And this project was actually completed at the begining of 2018.

Caffe-MaskYolo

What I add in this version of caffe?

• Demos for object detection, mask segmentation and keypoints recognition
• YOLO v2 (RegionLossLayer) and v3 (YoloLossLayer) are supported
• Instance Mask segmentation with Yolo
• Keypoints Recognition with yolo
• training data preparation and training

preparation

# clone
git clone https://github.com/leon-liangwu/MaskYolo_Caffe.git --recursive

# install requirements
cd ROOT_MaskYolo
pip install -r requirements.txt

# compile box_utils
cd lib/box_utils
python setup.py build_ext --inplace

# compile caffe
cd caffe-maskyolo
cp Makefile.config.example Makefile.config
make -j
make pycaffe

download pretrained models

Click DropBox or WeiYun to download pretrained models

cd ROOT_MaskYolo
tar zxvf /your/downlaod/model/path/pretrained_models.tgz ./models_maskyolo/

Object Detection with YOLO

support to use yolo v2 or v3 to detect objects in images

objection demo

cd tools
python yolo_inference.py [--img_path=xxx.jpg] [--model=xxx.prototxt] [--weights=xxx.caffemodel]
# Net forward time consumed: 3.96ms

The demo result is shown below.

train for object detection

cd ROOT_MaskYolo

# prepare voc labels, set the categories you want to detect in scripts/voc_label.py
python scripts/voc_label.py --voc_dir=/path/to/voc_dir/

# generate lmdb for detection
sh ./scripts/convert_detection.sh  /path/to/train.txt /path/to/lmdb   

# train the detection model
cd ./models/mb_v2_t4_cls5_yolo/
sh train_yolo.sh

Yolo with Instance Masks

detection and instance mask demo

cd tools
python mask_inference.py [--img_path=xxx.jpg] [--model=xxx.prototxt] [--weights=xxx.caffemodel] 
# Net forward time consumed: 8.67ms

train for mask regression

# compile the pythonapi of cocoapi
cd ROOT_MaskYolo/lib/cocoapi/PythonAPI
make -j

# use the following command to generate lmdb which contains mask and keypoints information
cd ROOT_MaskYolo
python scripts/createdata_mask.py --coco_dir=/path/to/coco --lmdb_dir=/path/to/lmdb

# the training for mask consists of 2 steps 
cd ./models_maskyolo/mb_body_mask

# 1. freeze the weights of detection network, only update the roi mask part
sh train_maskyolo_step1.sh

# 2. update all the network with finetuning the model of step1
sh train_maskyolo_step2.sh

Try your trained model with

python mask_inference.py --weights=xxx.caffemodel

Yolo with Instance Keypoints

detection and keypoints demo

cd tools

python kps_inference.py [--img_path=xxx.jpg] [--model=xxx.prototxt] [--weights=xxx.caffemodel] 
# Net forward time consumed: 5.58ms

Reference

You Only Look Once: Unified, Real-Time Object detection http://arxiv.org/abs/1506.02640

YOLO9000: Better, Faster, Stronger https://arxiv.org/abs/1612.08242

YOLOv3: An Incremental Improvement https://pjreddie.com/media/files/papers/YOLOv3.pdf

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Mask R-CNN