yolo-v4-tf.keras

A simple tf.keras implementation of YOLO v4

TODO

• Cosine annealing scheduler
• mAP
• Mosaic augmentation
• DropBlock
• Self-adversarial training (SAT)
• Label smoothing
• Mish
• IoU, GIoU, CIoU loss
• multi-GPU training

Quick Start

1. Download official YOLO v4 pre-trained weights from github/AlexeyAB/darknet

2. Initialize YOLO model and load weights

3. 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

1. 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>
   

2. 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
   ...
   

3. Generate class name file, # of lines == # of classes

   Example: coco_classes.txt

   person
   bicycle
   car
   motorbike
   aeroplane
   bus
   ...
   

4. 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=[])

Acknowledgements

• qqwweee/keras-yolo3
• AlexeyAB/darknet
• hunglc007/tensorflow-yolov4-tflite
• Cartucho/mAP
• miemie2013/Keras-YOLOv4
• david8862/keras-YOLOv3-model-set
• Ma-Dan/keras-yolo4