Everything You Need to Know About YOLOv3: The Complete Training Process, Model Performance.

6 min readMar 13, 2023

The YOLOv3 (You Only Look Once version 3) is a real-time object detection system that is capable of detecting 80 common object classes present in the COCO (Common Objects in Context) dataset. The architecture of YOLOv3 consists of several components including a deep convolutional neural network, anchor boxes, and non-maximum suppression.

The convolutional neural network is responsible for learning the features of the image, extracting high-level representations of the objects, and then making predictions about the objects in the image. The network has 53 convolutional layers and is trained on the COCO dataset to detect 80 object classes.

Object detection is a popular computer vision task that involves identifying and localizing objects within an image. YOLOv3 is a state-of-the-art object detection model that has achieved impressive results on a wide range of datasets. In this blog post, we’ll dive deep into YOLOv3, exploring its training process, model performance, advantages, and disadvantages.

Introduction to YOLOv3

YOLOv3 stands for “You Only Look Once version 3”. As the name suggests, YOLOv3 is a one-stage object detection model that can detect multiple objects within an image in a single forward pass. This makes YOLOv3 much faster than other object detection models, such as Faster R-CNN and Mask R-CNN, which are two-stage models that require multiple passes over an image.

YOLOv3 uses a deep neural network architecture that combines convolutional neural networks (CNNs) and residual networks (ResNets). The architecture consists of a backbone network, a neck network, and a head network. The backbone network is responsible for extracting features from an input image, the neck network is responsible for fusing features across different scales, and the head network is responsible for predicting object bounding boxes and class probabilities.

The Training Process

Training a YOLOv3 model involves several steps:

Step 1: Data Collection and Annotation

The first step in training a YOLOv3 model is to collect a dataset of images and annotate the objects within those images. Annotation involves labeling the object class, object location (in terms of a bounding box), and object size within the image. YOLOv3 requires that annotations are provided in the form of XML files.

Step 2: Data Augmentation

Once the dataset is annotated, the next step is to augment the data. Data augmentation involves applying various transformations to the original images, such as flipping, rotating, scaling, and cropping. Data augmentation helps to increase the size of the training set and makes the model more robust to variations in the input data.

Step 3: Model Training

Data Preparation: YOLOv3 requires a dataset of annotated images to train on. The annotations typically include bounding box coordinates and class labels for each object in the image. The dataset is split into a training set and a validation set.
Network Architecture: The YOLOv3 architecture consists of a backbone network, which is used to extract features from the input image, and a detection head, which generates predictions from the extracted features. The backbone network is typically a pre-trained convolutional neural network, such as Darknet-53, and the detection head consists of a series of convolutional layers, up-sampling layers, and shortcut connections.
Loss Function: The loss function used to train YOLOv3 is a combination of three components: a localization loss, a confidence loss, and a classification loss. The localization loss penalizes the difference between the predicted and ground-truth bounding box coordinates, the confidence loss penalizes the difference between the predicted and ground-truth objectness scores, and the classification loss penalizes the difference between the predicted and ground-truth class probabilities.
Hyper-parameter Tuning: The training process for YOLOv3 involves tuning several hyper-parameters, including the learning rate, the batch size, and the number of training epochs. These hyper-parameters can significantly impact the performance of the trained model.
Model Evaluation: Once the training process is complete, the trained model is evaluated on the validation set using metrics such as mean average precision (mAP) and intersection over union (IoU).

IOU: Intersection over union

NMS: Non Max Suppression

Math Intuition of YOLOv3

The math behind YOLOv3 is based on the concept of anchor boxes. Anchor boxes are pre-defined bounding boxes of different sizes and aspect ratios that are used to predict the final bounding box coordinates. YOLOv3 predicts four values for each anchor box: the x and y coordinates of the center of the box, the width, and the height. It also predicts a confidence score for each box, which indicates the likelihood of the box containing an object, and a class probability for each box, which indicates the probability of the object belonging to a particular class.

Image Grids in Yolov3

In YOLOv3, the input image is divided into a grid of cells, and each cell is responsible for detecting objects that are present in that particular region of the image. The size of the grid is determined by the network architecture, and in YOLOv3, the default grid size is 13 x 13.

This means that the image is divided into 13 x 13 = 169 cells, and each cell is responsible for detecting objects that are present in its region. Each cell predicts a fixed number of bounding boxes (typically 3 in YOLOv3), and for each bounding box, it predicts the x and y coordinates of the box’s center relative to the cell’s top left corner, the width and height of the box, and the probability that the box contains an object.

The predictions from all the cells are then combined to form the final output of the network. The predicted bounding boxes are filtered based on their confidence score and non-maximum suppression is applied to remove duplicate detection.

By dividing the image into a grid of cells and using a single network to predict bounding boxes, YOLOv3 is able to achieve real-time object detection on a wide range of devices. However, using a coarse grid size like 13 x 13 may result in lower detection accuracy for small objects or objects that span multiple cells. To address this, YOLOv3 also includes features like anchor boxes and feature pyramid networks to improve detection accuracy.

Model Performance

YOLOv3 has achieved state-of-the-art performance on several popular object detection datasets, including COCO and Pascal VOC. YOLOv3 has an mAP of 57.9% on the COCO dataset, which is significantly higher than other one-stage object detection models, such as SSD and Retina Net.