keras-yolo3-Mobilenet

Introduction

A Keras implementation of YOLOv3 (Tensorflow backend) inspired by allanzelener/YAD2K.

And I change the backend of darknet53 into

√ Mobilenet

inceptionResnetv2

inceptionv3

Guide of keras-yolov3-Mobilenet

1.train_Mobilenet.py

 Code for training
 (I change some of the code to read in train.txt and val.txt seperately, remember to change that, and the .txt file are in the same form descibed below)

2.yolo3/model_Mobilenet.py

model_Mobilenet is the yolo model based on Mobilenet
##### If you want to go through the source code,ignore the other function,please see the yolo_body ####
(I extract three layers from the Mobilenet to make the prediction)

3.yolo_Mobilenet.py

testing and evaluation code

Be sure that you do not load pretrained model when training because I did it on keras_applications,and the keras will load the pretrained model for you

if you find anything tricky, contact me as you wish

Evaluation

Please use this repo to draw the RP curve calculate the MAP https://github.com/Adamdad/Object-Detection-Metrics.git

Guide of keras-yolov3

[this is the guide for darknet53 not mobilenet]

Quick Start

Download YOLOv3 weights from YOLO website.
Convert the Darknet YOLO model to a Keras model.
Run YOLO detection.

wget https://pjreddie.com/media/files/yolov3.weights
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
python yolo_video.py [OPTIONS...] --image, for image detection mode, OR
python yolo_video.py [video_path] [output_path (optional)]

For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with --model model_file and --anchors anchor_file.

Usage

Use --help to see usage of yolo_video.py:

usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
                     [--classes CLASSES] [--gpu_num GPU_NUM] [--image]
                     [--input] [--output]

positional arguments:
  --input        Video input path
  --output       Video output path

optional arguments:
  -h, --help         show this help message and exit
  --model MODEL      path to model weight file, default model_data/yolo.h5
  --anchors ANCHORS  path to anchor definitions, default
                     model_data/yolo_anchors.txt
  --classes CLASSES  path to class definitions, default
                     model_data/coco_classes.txt
  --gpu_num GPU_NUM  Number of GPU to use, default 1
  --image            Image detection mode, will ignore all positional arguments

MultiGPU usage: use --gpu_num N to use N GPUs. It is passed to the Keras multi_gpu_model().

Training

Generate your own annotation file and class names file.
One row for one image;
Row format: image_file_path box1 box2 ... boxN;
Box format: x_min,y_min,x_max,y_max,class_id (no space).
For VOC dataset, try python voc_annotation.py
Here is an example:
```
path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
path/to/img2.jpg 120,300,250,600,2
...
```
Make sure you have run python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
The file model_data/yolo_weights.h5 is used to load pretrained weights.
Modify train.py and start training.
python train.py
Use your trained weights or checkpoint weights with command line option --model model_file when using yolo_video.py Remember to modify class path or anchor path, with --classes class_file and --anchors anchor_file.

If you want to use original pretrained weights for YOLOv3:
1. wget https://pjreddie.com/media/files/darknet53.conv.74
2. rename it as darknet53.weights
3. python convert.py -w darknet53.cfg darknet53.weights model_data/darknet53_weights.h5
4. use model_data/darknet53_weights.h5 in train.py

Some issues to know

The test environment is
- Python 3.5.2
- Keras 2.1.5
- tensorflow 1.6.0
Default anchors are used. If you use your own anchors, probably some changes are needed.
The inference result is not totally the same as Darknet but the difference is small.
The speed is slower than Darknet. Replacing PIL with opencv may help a little.
Always load pretrained weights and freeze layers in the first stage of training. Or try Darknet training. It's OK if there is a mismatch warning.
The training strategy is for reference only. Adjust it according to your dataset and your goal. And add further strategy if needed.
For speeding up the training process with frozen layers train_bottleneck.py can be used. It will compute the bottleneck features of the frozen model first and then only trains the last layers. This makes training on CPU possible in a reasonable time. See this for more information on bottleneck features.

guapizyq / keras-YOLOv3-mobilenet