Comparing YOLO-NAS and YOLOv8: A Comprehensive Analysis of Two State-of-the-Art Object Detection Models

Comparing YOLO-NAS and YOLOv8: A Comprehensive Analysis of Two State-of-the-Art Object Detection Models

Discover the differences between YOLO-NAS and YOLOv8, two cutting-edge object detection models. Explore their strengths, weaknesses, and performance in small object detection, localization accuracy, and real-time edge-device applications. Make an informed decision about which model is best suited for your computer vision needs.
Ufuk Dag
4 min

Object detection, a critical component of computer vision, has seen significant progress in recent years. Among the popular and efficient object detection models are the YOLO series. This article aims to compare two of the latest models: YOLO-NAS and YOLOv8. By examining their key differences, strengths, and weaknesses, we will assist you in determining which model is most suitable for your requirements.

1. Model Overview


YOLO-NAS is an advanced foundational model for object detection, drawing inspiration from YOLOv6 and YOLOv8. It introduces a new quantization-friendly basic block, advanced training schemes, post-training quantization, AutoNac optimization, and pre-training on top datasets. YOLO-NAS significantly enhances small object detection, localization accuracy, and performance-per-compute ratio. It excels in real-time edge-device applications and outperforms existing YOLO models on diverse datasets.

1.2 YOLOv8

YOLOv8 is the latest iteration in the YOLO series, building upon the success of its predecessors. It introduces a new transformer-based architecture, resulting in improved accuracy and performance. YOLOv8 incorporates 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 enhance quantization performance compared to earlier models. This new block enables YOLO-NAS to achieve higher accuracy while maintaining efficiency.

YOLOv8 utilizes a transformer-based architecture that distinguishes it from previous YOLO models. This innovative design has led to accuracy and performance improvements. However, YOLOv8 does not incorporate the quantization-friendly basic block found in YOLO-NAS. The complete extent of its architecture is yet to be revealed, as the eagerly anticipated academic paper by Glen Jocher from Ultralytics has not been released...twice! Let's hope that HAT is a delicious treat!

3. Training Schemes and Pre-Training

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

YOLOv8 also employs an advanced training scheme, including knowledge distillation and pseudo-labeling. However, it lacks the pre-training on Object365, etc., employed by YOLO-NAS, which might affect 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 training, making it more efficient. It is comparable to compressing a large, high-quality image into a smaller file size that loads faster and takes up less space on your device while still looking good.

YOLO-NAS supports post-training quantization (PTQ), converting the network to INT8 after training. This process enhances the model's efficiency without sacrificing accuracy.

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

5. AutoNac Optimization

Autonac optimization is an intelligent technique used in computer vision to improve algorithm performance. It is akin to a master chef fine-tuning ingredients and cooking processes to create the most delicious dish. In computer vision, Autonac explores different configurations of model components, searching for the most efficient and accurate setup. By doing so, it helps create powerful and fast algorithms that effectively process and understand images or videos, making our model more impressive and useful.

6. Performance Comparison

The performance comparison between YOLO-NAS and YOLOv8 in terms of mean average precision (mAP) and latency (in milliseconds) is summarized in the following table:

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

It is important to note that these numbers are approximate and may vary depending on the source and specific test conditions. For the most accurate and up-to-date information, it is recommended to refer to the latest official documentation and research papers.

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 an excellent choice for real-time edge-device applications due to its efficiency, accuracy, and performance-per-compute ratio. The model's support for post-training quantization (PTQ) and the inclusion of a quantization-friendly basic block further enhance its suitability for such applications.

On the other hand, YOLOv8 is not as well-suited for real-time edge-device applications as YOLO-NAS. Its lack of support for PTQ and lower efficiency compared to its competitor might limit its performance in scenarios that require lower computational resources. However, it is worth mentioning that YOLOv8 is capable of running on embedded devices like the OpenCV AI Kit.

9. Summing it all up

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

As the field of object detection continues to evolve, it is crucial to stay informed about the latest models and their capabilities. 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.

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