After YOLOv8, there’s yet a new and better state-of-the-art object detection model, YOLO-NAS. It is an object detection algorithm developed by Deci AI to tackle the limitations of the previous YOLO (You Only Look Once) models.
The model is built from AutoNAC, a Neural Architecture Search Engine. It surpasses the speed and performance of SOTA models, which presents a big leap in object detection by improving the accuracy-latency and quantization support tradeoffs.
This article will discuss the strengths and features of YOLO-NAS, exploring why it is the best object detection model in the YOLO series.
Understanding How YOLO-NAS Works
Deci’s AutoNAC (Automated Neural Architecture Construction) generates YOLO-NAS’s architecture. AutoNAC is a seamless process that provides a performance boost to existing deep neural networks.
The AutoNAC pipeline takes a user-trained deep neural network as an input, a dataset, and access to an inference system. The user-trained deep neural network then undergoes redesigning using the pipeline. An optimized architecture with lower latency is then achieved without compromising accuracy.
YOLO-NAS uses RepVGG. RepVGG makes it available for optimization post-training by re-parameterization or Post-training Quantization. It is a type of neural network architecture based on VGG. It uses regularization techniques designed to enhance deep learning models’ generalization ability.
The architecture’s design is more efficient in speed and memory. RepVGG undergoes training using a multi-branch architecture to achieve a faster inference. It is then converted to a single branch using re-parameterization.
This feature makes YOLO-NAS very useful for production deployment. This is because it is possible to train and optimize the model with complete precision for inference speed and memory usage.
Key Features of YOLO-NAS
YOLO-NAS’s key features include the following:
Comparative Analysis: YOLO-NAS vs Other YOLO Models
Below is a comparison between various YOLO series models.
YOLO-NAS is better than pre-existing object detection models, but it comes with its cons. Here is a list of the benefits and demerits of YOLO-NAS:
Implementation of YOLO-NAS
You will useGoogle Colabto write and run the codes in this implementation. An alternative for Google Colab would be tocreate a virtual environmentanduse an IDE on your local machine.
The model is very resource intensive. Ensure you have at least 8GB RAM before running it on your machine. The bigger the video size, the more memory it uses.
Installing Dependencies
Install the YOLO-NAS dependency,super-gradientsusing the command below:
After a successful installation, you will now be able to train the YOLO-NAS model.
Model Training
To train the model, run the code block below:
You are importing the training model from the installed SuperGradients library in this code. You are then using the pre-trained model weights from the COCO dataset.
Model Inference
The model inference has potential applications for image detection, classification, and segmentation tasks.
In this case, you will focus on theobject detection taskfor videos and images. To detect an object in an image, run the code block below:
Make sure you use the accurate path to your image. In this case, upload the image on the Google Colab environment, then copy the path of the image.
Expected output:
To detect objects in a video, run the code block below:
Like the image data, upload the video you want its objects detected to the Google Colab environment and copy the path to the video variable. The predicted video will be accessible from the Google Colab environment using the namedetected.mp4. Download the video to your local machine before ending your session.
YOLO-NAS also supports model fine-tuning and training on custom data. The documentation is available onDeci’sfine-tuning starter notebook.
Real-World Applications of YOLO-NAS
YOLO-NAS’ source code is accessible under Apache License 2.0, which is accessible for non-commercial use. For commercial use, the model should undergo retraining from scratch to get custom weights.
It is a versatile model whose application is possible in several fields, such as:
Autonomous Vehicles and Robotics
YOLO-NAS can improve the perception capabilities of autonomous vehicles, enabling them to detect and track objects faster and more accurately in real-time. This ability helps to ensure road safety and a smooth driving experience.
Surveillance and Security Systems
The model can provide fast, accurate, and real-time object detection for surveillance and security systems, which helps identify potential threats or suspicious activities, resulting in better security systems
Retail and Inventory Management
The model can provide fast and accurate object detection capabilities that allow efficient and real-time automated inventory management, stock tracking, and shelf optimization. This model helps to reduce operating costs and increase profits.
Healthcare and Medical Imaging
In healthcare, YOLO-NAS is capable of helping in the efficient detection and analysis of disease anomalies or specific areas of interest. The model can help doctors to accurately diagnose diseases and monitor patients, thus improving the healthcare sector.
YOLO-NAS Takeaway
YOLO-NAS is a new object detection model pioneering a new way of performing object detection. It is better than the SOTA models. Its performance on object detection is a big leap for computer vision projects.
