YOLO-NAS vs. YOLOv8: A Comprehensive Comparison

Introduction

Object detection, a crucial aspect of computer vision, has witnessed significant advancements in recent years. Among the most popular and efficient object detection models are the YOLO series. In this article, we'll compare two of the latest models: YOLO-NAS and YOLOv8. We'll examine their key differences, strengths, and weaknesses to help you decide which model is the best fit for your needs.

1. Model Overview

1.1 YOLO-NAS

YOLO-NAS is a cutting-edge foundation model for object detection, inspired by YOLOv6 and YOLOv8. It boasts a new quantization-friendly basic block, advanced training schemes, post-training quantization, AutoNac optimization, and pre-training on top datasets. YOLO-NAS significantly improves small object detection, localization accuracy, and performance-per-compute ratio. It is ideal for real-time edge-device applications and outperforms existing YOLO models on diverse datasets.

1.2 YOLOv8

YOLOv8 is the latest version in the YOLO series, building upon the success of previous models. It introduces a new transformer-based architecture, which results in improved accuracy and performance. YOLOv8 boasts an advanced training scheme with knowledge distillation and pseudo-labeling, making it a powerful object detection model.

2. Architecture and Basic Blocks

YOLO-NAS features a novel quantization-friendly basic block, designed to improve quantization performance compared to its predecessors. This new block allows YOLO-NAS to achieve higher accuracy while maintaining efficiency.

YOLOv8 employs a transformer-based architecture that sets it apart from earlier YOLO models. This innovative design has led to improvements in accuracy and performance. However, it does not incorporate the quantization-friendly basic block found in YOLO-NAS. We're still waiting to grasp the complete extent of its architecture, as the much-anticipated academic paper by Glen Jocher from Ultralytics has yet to be released... twice! Here's hoping that HAT is a tasty treat, my friend.

3. Training Schemes and Pre-Training

YOLO-NAS undergoes pre-training on COCO, Object365 dataset & Roboflow 100, leverages pseudo-labeled data, and benefits from knowledge distillation using a pre-trained teacher model. This advanced training scheme helps YOLO-NAS achieve high accuracy and efficiency.

YOLOv8 also utilizes an advanced training scheme, including knowledge distillation and pseudo-labeling. However, it lacks the pre-training on Object365, etc. that YOLO-NAS employs, potentially impacting its performance in certain object detection tasks.

4. Post-Training Quantization

Post-Training Quantization (PTQ) is a technique that simplifies a computer vision model after it's been trained, making it more efficient. It's like compressing a large, high-quality image into a smaller file size that still looks good, but loads faster and takes up less space on your device. 

YOLO-NAS supports post-training quantization (PTQ), converting the network to INT8 after training. This process makes the model even more efficient, without sacrificing accuracy.

YOLOv8 does not currently support PTQ, which may limit its efficiency in certain applications, particularly those requiring lower computational resources.

5.AutoNac Optimization

Autonac optimization is a smart technique used in computer vision to improve the performance of algorithms. It's like a master chef who can take a recipe and fine-tune the ingredients and cooking process to create the most delicious dish possible. In the case of computer vision, Autonac explores different ways of arranging and connecting the components of a model, searching for the most efficient and accurate configuration. By doing so, it helps to create powerful and fast algorithms that can process and understand images or videos more effectively, making our model even more impressive and useful.

YOLO-NAS incorporates AutoNac optimization, which leads to three final networks using the equivalent GPU time of training just five networks. This optimization process makes YOLO-NAS more efficient and effective.

YOLOv8 does not utilize AutoNac optimization, which may impact its overall efficiency compared to YOLO-NAS.

6. Performance Comparison

The following table provides a comparison of YOLO-NAS and YOLOv8 in terms of mAP (mean average precision) and latency (in milliseconds): 

According to the performance comparison, YOLO-NAS S and M variants outperform their YOLOv8 counterparts in terms of mAP. However, YOLOv8 L has a slightly higher mAP compared to YOLO-NAS L. In terms of latency, YOLO-NAS consistently performs faster than YOLOv8 across all sizes.

Please note that these numbers are approximate and may vary depending on the source and the specific test conditions. Always refer to the latest official documentation and research papers for the most accurate and up-to-date information.

7. Small Object Detection and Localization Accuracy

YOLO-NAS excels in detecting small objects and offers improved localization accuracy. These enhancements contribute to its overall superiority in various use cases, particularly those involving small or hard-to-detect objects.

While YOLOv8 is an impressive object detection model, it falls short in detecting small objects and localization accuracy compared to YOLO-NAS.

 

8. Real-Time Edge-Device Applications

YOLO-NAS is ideal for real-time edge-device applications due to its efficiency, accuracy, and performance-per-compute ratio. The model's PTQ and quantization-friendly basic block further enhance its suitability for such applications.

YOLOv8 is not as well-suited for real-time edge-device applications as YOLO-NAS, due to its lack of PTQ and lower efficiency compared to its competitor. However, it is able to run on embedded devices like the OpenCV AI Kit.

9. Summing it all up

In conclusion, YOLO-NAS and YOLOv8 are both powerful and efficient object detection models. However, YOLO-NAS outperforms YOLOv8 in several key areas, including small object detection, localization accuracy, post-training quantization, and real-time edge-device applications. If you're looking for a cutting-edge object detection model with higher accuracy, faster processing, and greater efficiency, YOLO-NAS is a clear winner.

As the field of object detection continues to evolve, staying informed about the latest models and their capabilities is essential. By comparing YOLO-NAS and YOLOv8, you can make an informed decision about which model best suits your needs and take advantage of the latest advancements in computer vision technology.

10. How to build YOLO-NAS apps?

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