YOLO V2 with TensorFlow 2.0

Here is a jupyter notebook featuring a complete implementation from scratch of YOLOV2 with TensorFlow 2.0 :

Dataset pipeline with data augmentation
Training from YOLO pretrained weights
Visualization of object detection

I use this notebook to train a model to detect crop and weeds in a field. The goal is to detect crop in real time for tractor guidance and to detect weeds to remove them.

Original paper : YOLO9000: Better, Faster, Stronger by Joseph Redmond and Ali Farhadi.

Files

Yolo_V2_tf_2.ipynb : Yolo V2 implementation with Tensorflow 2.0
Yolo_V2_tf_eager.ipynb : old notebook, Yolo V2 implementation with Tensorflow 1.x with eager execution

Requirements

tensorflow 2.0
imgaug
cv2

Before using the notebook

Download pretrained weights here. Place this weights file in notebook directory and name it yolo.weights
The training requires four directories containing images and annotations :

train_image_folder/ : contains images files used during training (png format)

train_annot_folder/ : contains annotations in PASCAL VOC format (one xml file for each image)

val_image_folder/ : contains images files used for validation

val_annot_folder/ : contains annotations in PASCAL VOC format

Using the notebook

Define object's labels to detect (same labels as defined in PASCAL VOC xml file). Example :

	LABELS           = ('sugarbeet', 'weed')

Define image size in dataset and YOLO grid size. Image size must be YOLO grid size * 32.

	IMAGE_H, IMAGE_W = 512, 512
	GRID_H,  GRID_W  = 16, 16 # GRID size = IMAGE size / 32

Define train batch size and validation batch size : depends on image size and video card RAM.

	TRAIN_BATCH_SIZE = 10
	VAL_BATCH_SIZE   = 10

Define path to dataset directories.

	# Train and validation directories

	train_image_folder = 'data/train/image/'
	train_annot_folder = 'data/train/annotation/'
	val_image_folder = 'data/val/image/'
	val_annot_folder = 'data/val/annotation/'