Easy Keras Yolo V3

Test platform

• Win10, tensorflow-cpu 1.9.0,keras 2.1.5, python 3
• Ubuntu 14.04, tensorflow-gpu 1.10.1, keras 2.2.4,python3, cuda-9.0 on GTX1080

Introduction

This repo is intended for purely training your own dataset of detection. And this repo is mainly modified from qqwweee/keras-yolo3.

Prepare own dataset

Training on own dataset is quite simple, first download (choose one)

• China Jianguoyun yolo_weights.h5
• Google Drive yolo_weights.h5

into model_data/

And provide model_data/own_classes.txt which contains the class name of detecting objects, here I provide a example for only one class detection task.

And then all you need is to prepare own_train.txt in the same directory with train.py, each line of train.txt is of this format: /pat/to/img1 xmin,y_min,x_max,y_max,id, remember no <space> before/after , and an <space> between /path/to/img1 and xmin. Here I provide a example own_train.txt, remember id starts from 0.

like imgs/1.jpg 147,30,437,215,0 147,30,437,215,1 for two objects labelled in one image, <space> shall be inserted between two labels.

Training

python train.py will start training, I recommend reading train.py carefully before starting. If memory out, change batchsize in train.py

The model trained will be stored in logs/000/ , please check.

Inference

After training, yolo_detect.py will detect objects on image while you type the image path.

Usage:

python yolo_detect.py --model_path /path/to/models, remember to use the path to models trained stored in logs/000/ mentioned above.

My Dataset Performance

I trained my model on detecting box of input image. My dataset size is 500. And I got loss of 10 after training. The result is shown below. You can download my trained model in (choose one)

• China Jianguoyun trained_weights_final.h5
• Google Drive trained_weights_final.h5

and put it into model_data/