yolo-v4-tf.keras

A simple tf.keras implementation of YOLO v4

TODO

Quick Start

Download official YOLO v4 pre-trained weights from github/AlexeyAB/darknet
Initialize YOLO model and load weights
Run prediction

Example: Inference.ipynb:

from models import Yolov4
model = Yolov4(weight_path='yolov4.weights', 
               class_name_path='class_names/coco_classes.txt')
model.predict('input.jpg')

Training

Generate your annotation files (.XML) in VOC format for each images

HINT: An easily used annotation tool: labelImg

Example: A 2 object xml file

<annotation>
    <folder>train_img2</folder>
    <filename>yui.jpg</filename>
    <path>/Users/taipingeric/dataset/train_img2/yui.jpg</path>
    <source>
        <database>Unknown</database>
    </source>
    <size>
        <width>465</width>
        <height>597</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    <object>
        <name>person</name>
        <pose>Unspecified</pose>
        <truncated>1</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>43</xmin>
            <ymin>41</ymin>
            <xmax>430</xmax>
            <ymax>597</ymax>
        </bndbox>
    </object>
    <object>
        <name>person</name>
        <pose>Unspecified</pose>
        <truncated>1</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>60</xmin>
            <ymin>70</ymin>
            <xmax>20</xmax>
            <ymax>207</ymax>
        </bndbox>
    </object>
</annotation>

Convert all XML files to a single .txt file:

Row format: img_path BOX0 BOX1 BOX2 ...

BOX format: xmin,ymin,xmax,ymax,class_id

Example: xml_to_txt.py
```
img1.jpg 50,60,70,80,0 70,90,100,180,2
img2.jpg 10,60,70,80,0
...
```
Generate class name file, # of lines == # of classes

Example: coco_classes.txt
```
person
bicycle
car
motorbike
aeroplane
bus
...
```
Train with the code below

Example: train.ipynb

from utils import DataGenerator, read_annotation_lines
from models import Yolov4

train_lines, val_lines = read_annotation_lines('../dataset/txt/anno-test.txt', test_size=0.1)
FOLDER_PATH = '../dataset/img'
class_name_path = '../class_names/bccd_classes.txt'
data_gen_train = DataGenerator(train_lines, class_name_path, FOLDER_PATH)
data_gen_val = DataGenerator(val_lines, class_name_path, FOLDER_PATH)

model = Yolov4(weight_path=None, 
               class_name_path=class_name_path)

model.fit(data_gen_train, 
          initial_epoch=0,
          epochs=10000, 
          val_data_gen=data_gen_val,
          callbacks=[])

Reference

The repositories I've admired and learned a lot

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A simple tf.keras implementation of YOLO v4

MIT License

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