Computer Vision: Unraveling Object Detection with KerasCV's YOLOv8 Integration?
By Celia Lozano, Head of Data & AI at Bosonit
Hello Bosotrends!
Welcome aboard to a detailed voyage into object detection, courtesy of the newly integrated "KerasCV YOLOv8" model. YOLO, an acronym for "You Only Look Once," has revolutionized object detection, with its applications spanning from surveillance systems to the dynamic realm of autonomous vehicles.
But here's a twist: have you ever pondered the outcomes when the prowess of YOLOv8 marries the versatility of the KerasCV framework? The recent synergy between KerasCV and the illustrious YOLOv8 detection model has birthed an exciting arena for developers and enthusiasts alike. Today, we shall dive deep, exploring the intricacies of fine-tuning the YOLOv8 model on a bespoke dataset. Our itinerary includes:?
The YOLO Legacy?
Standing for "You Only Look Once", YOLO burst onto the scene as a novel approach to object detection. Traditionally, object detection involved a two-step process: proposing regions and then classifying them. YOLO changed the game by transforming it into a single regression problem. With YOLO, the model processes the whole image in one go, making it significantly faster without compromising on accuracy.?
YOLOv1 laid the foundation, but with YOLOv2 (YOLO9000) and YOLOv3, the architecture underwent refinements, becoming more accurate and faster. YOLOv4 took this a notch higher with better optimization, leading to faster inference times and improved detection rates. The launch of YOLOv8 represents the culmination of this journey, with state-of-the-art accuracy and unparalleled speeds.?
KerasCV – A Torchbearer in the Deep Learning Landscape?
While YOLO was cementing its legacy in object detection, Keras was making strides as an accessible deep learning framework. KerasCV, an offspring of the primary Keras project, aimed at easing computer vision tasks, offering tools, utilities, and pre-trained models out-of-the-box. As Keras gained traction, KerasCV emerged as a hub for developers seeking pre-built functionalities and models specifically tailored for computer vision.
The Traffic Light Dataset at a Glance?
Our odyssey begins with the Small Traffic Light Dataset (S2TLD) – a treasure trove curated by Thinklab. This dataset, featuring a plethora of 4,564 images annotated in the ubiquitous XML format, serves as our canvas for training the KerasCV YOLOv8 model. An ensemble of images, as seen below, encapsulates diverse scenarios, giving a holistic view of traffic lights from myriad angles and conditions.?
Categorically Speaking?
This dataset version distinguishes traffic lights into four intuitive categories:?
Setting the Stage for KerasCV YOLOv8 Object Detection?
Let’s begin with the setup of the necessary libraries.
Before we plunge into the heart of object detection, it's paramount to establish our toolbox. Initiating our journey, we synchronize our environment with the "KerasCV YOLOv8" toolkit. As we lay the foundational bricks with TensorFlow and Keras, additional tools from the KerasCV repository fortify our setup. With these essentials, like bounding_box and visualization, in our arsenal, we're set to navigate through the myriad avenues of object detection seamlessly.?
Dataset Acquisition & Model Initialization?
After procuring our dataset, we chalk out parameters crucial for training, such as the dataset split, batch size, and learning rate, setting the stage for 75 epochs of rigorous training. The crescendo of our journey sees the YOLOv8 model, endowed with a COCO pre-trained backbone, being primed for action.?
Deciphering the Outcomes?
Post-training, it's time to scrutinize our model's prowess. Our chosen yardstick? The Mean Average Precision (mAP) – a revered metric in object detection. With the diligent implementation of mAP by KerasCV for all object detection models, our evaluation is both rigorous and insightful.?
Conclusion?
As the curtain drops on our exploration, it's evident that the amalgamation of KerasCV and YOLOv8 has ushered in a new era for object detection. With this integration embedded into KerasCV's core, the horizons for TensorFlow and Keras developers have expanded exponentially. Now, as we sign off, we're eager to hear about your next venture. Drop your thoughts in the comments below!?