Motivation

Objectives

• YOLOv8 is a version of YOLO by Ultralytics. 

• As a cutting-edge, state-of-the-art (SOTA) model, YOLOv8 builds on the success of previous versions, introducing new features and improvements for enhanced performance, flexibility, and efficiency. 

• YOLOv8 supports a full range of vision AI tasks, including detection, segmentation, pose estimation, tracking, and classification. 

• This versatility allows users to leverage YOLOv8's capabilities across diverse applications and domains.

Related Works

Idea

1. Replace the C3 module with the C2f module.

2. Replace the first 6x6 Conv with 3x3 Conv in the Backbone (Previously, Focus layer).

3. Delete two Convs (No.10 and No.14 in the YOLOv5 config).

4. Replace the first 1x1 Conv with 3x3 Conv in the Bottleneck.

5. Use decoupled head and delete the objectness branch.

Steps

Architecture

Input 

• 640x640 Images

Data Augmentation

Backbone

• Modified CSPDarknet53.

• The YOLOv8 architecture makes use of a few key components to perform object detection tasks. The Backbone is a series of convolutional layers that extract relevant features from the input image. The SPPF layer and the subsequent convolution layers process features at a variety of scales, while the Upsample layers increase the resolution of the feature maps. The C2f module combines the high-level features with contextual information to improve detection accuracy. Finally, the Detection module uses a set of convolution and linear layers to map the high-dimensional features to the output bounding boxes and object classes. The overall architecture is designed to be fast and efficient, while still achieving high detection accuracy. As for the diagram legend, the rectangles represent layers, with the labels describing the type of layer (Conv, Upsample, etc.) and any relevant parameters (kernel size, number of channels, etc.). The arrows represent data flow between layers, with the direction of the arrow indicating the flow of data from one layer to the next.

Head

• Like YOLOX.

• Multiple Convolutional layers followed by a series of fully connected layers.

• Responsible for predicting bounding boxes, objectness scores, and class probabilities.

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• In YOLOv8, the head is designed to be decoupled, meaning that it processes objectness, classification, and regression tasks independently. This design allows each branch to focus on its respective task and improves the overall accuracy of the model. To process the feature maps, the head uses a series of convolutional layers, followed by a linear layer to predict the bounding boxes and class probabilities. The design of the head is optimized for speed and accuracy, with particular attention paid to the number of channels and kernel sizes of each layer to maximize performance. In terms of resources to learn more, I would recommend reading the original YOLOv3 and YOLOv4 papers and the YOLOv5 and YOLOv6 papers. Additionally, there are many resources available online, such as articles and videos, that explain the concepts behind object detection models in more detail.

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• YOLOv8 uses two separate heads to predict bounding boxes and classes during inference. TAL or Task Alignment Learning is a training approach that helps to align the two separate heads so that they can work together during inference. In YOLOv8, the T-head is not a separate module, but rather a combination of the two heads that are used in the network. The TAL approach helps to improve the accuracy of YOLOv8 by aligning the classification and localization scores. This is done by using supervised learning with a weighted combination of the classification and localization losses. During inference, the network outputs the bounding box coordinates and associated class probabilities for each detected object. I hope this helps clarify any confusion. Let me know if you have any more questions or concerns.

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Loss Function

• Detection: CIoU and DFL.

• Classification: BCE.

Training Strategy

Pre-trained Models

Anchor-free

Dataset

Metrics

Experimental Results

Ablations

1. Install Ultralytics:

# Only If you have already installed ultralytics

pip uninstall ultralytics 

#clone the repository

git clone https://github.com/ultralytics/ultralytics.git

# Install ultralytics package locally

cd ultralytics

python setup.py install

#After that, you can use the ultralytics package

yolo task=detect mode=predict model=yolov8n.pt source="https://www.youtube.com/watch?v=Zgi9g1ksQHc"

• Tracking:

  1. https://docs.ultralytics.com/modes/track/ 

• CLI:

  1. https://docs.ultralytics.com/usage/cli/