leon-liangwu / Maskyolo_caffe
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YOLO V2 & V3 , YOLO Combined with RCNN and MaskRCNN
Stars: ✭ 101
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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?
- [x] Demos for object detection, mask segmentation and keypoints recognition
- [x] YOLO v2 (RegionLossLayer) and v3 (YoloLossLayer) are supported
- [x] Instance Mask segmentation with Yolo
- [x] Keypoints Recognition with yolo
- [x] 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
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