yolo-v4-tf.keras

A simple tf.keras implementation of YOLO v4

TODO

• [X] Cosine annealing scheduler
• [X] mAP
• [ ] Mosaic augmentation
• [ ] DropBlock
• [ ] Self-adversarial training (SAT)
• [ ] Label smoothing
• [X] Mish
• [X] IoU, GIoU, CIoU loss
• [X] 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](https://github.com/qqwweee/keras-yolo3)
* [AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
* [hunglc007/tensorflow-yolov4-tflite](https://github.com/hunglc007/tensorflow-yolov4-tflite)
* [Cartucho/mAP](https://github.com/Cartucho/mAP)
* [miemie2013/Keras-YOLOv4](https://github.com/miemie2013/Keras-YOLOv4)
* [david8862/keras-YOLOv3-model-set](https://github.com/david8862/keras-YOLOv3-model-set)
* [Ma-Dan/keras-yolo4](https://github.com/Ma-Dan/keras-yolo4)