yolov3-ios

Using yolo v3 object detection on ios platform.

Example applications:

QuickStart:

Run tiny_model.xcodeproj in ios.

Training

The training process mainly consults qqwweee/keras-yolo3. We add yolov3 with Densnet.

1.Requirement

python 3.6.4
keras 2.1.5
tensorflow 1.6.0

2.Generate datasets

Generate datasets with VOC format. And try python voc_annotations.

3.Start training

cd yolov3_with_Densenet

For yolo model with darknet:

wget https://pjreddie.com/media/files/darknet53.conv.74
rename it as darknet53.weights
python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5
python yolov3_train.py, with model_data/darknet53_weights.h5 as pre-trained model

For yolo model with densenet：

python densenet_train.py, with model_data/dense121_weights.h5 as pre-trained model

Converting

1.Building environment

virtualenv -p /usr/bin/python2.7 keras_coreml_virt
source keras_coreml_virt/bin/activate
pip install protobuf
pip install tensorflow==1.6.0
pip install keras==2.1.5
pip install h5py
pip install coremltools==0.8.0

2.Convert .h5 model to .mlmodel

python coreml.py

Building project in Xcode

open tiny_model.xcodeproj with Xcode 9+
change the .mlmodel file and Target Menmbership

For yolo model with darknet or densenet

modify the code from line 43 to line 49 in YOLO.swift
change the labels and the anchors in Helpers.swift
run the project

For tiny model

just change the labels and run the project

gafr/yolov3-ios