The Ultimate YOLO-NAS Guide (2025): What It Is & How to Use
For years, the YOLO (You Only Look Once) family has been a key player in fast and accurate object detection.
These models, from the original up to more recent versions like YOLOv12, have largely been a product of clever methods in object detection where experts meticulously design, tweak, and iterate on network architectures to push performance boundaries.
But what if this traditional, human-centric design process has inherent limitations, especially when we demand not just speed and accuracy, but also peak efficiency on diverse hardware, often in quantized (low-precision) formats?
Have you ever wondered if there's a more optimal, perhaps even automated, way to discover the best possible architecture, rather than just refining existing ones?
Consider this: Reports and empirical evidence frequently show that while Post-Training Quantization (PTQ) can make models faster, it can also lead to an accuracy drop of 2-5% or even more for complex architectures not inherently designed for it.
This trade-off can be a significant barrier for deployment in sensitive applications.
YOLO-NAS, developed by Deci AI, represents this shift. It's not just another YOLO version; it’s the result of a totally different approach to model creation.
Instead of relying solely on manual design, YOLO-NAS leverages the power of Neural Architecture Search (NAS).
Comparison with other models on COCO dataset
In this guide, we'll explain how YOLO-NAS's new architecture works, performs object detection and compare it to YOLOv8 in real world.
What new approach is used in YOLO-NAS
This is where YOLO-NAS is unique. Instead of engineers manually designing every layer and connection in the neural network (which is complex and time-consuming), YOLO-NAS uses Neural Architecture Search (NAS).
Neural Architecture Search (NAS)
Imagine NAS as an automated architect: You tell it the goal (e.g., find the best architecture for object detection on a specific type of hardware) and the constraints (e.g., how fast it needs to run).
The NAS algorithm then intelligently explores thousands of possible network designs, testing and tweaking them automatically until it finds a highly optimized one.
But YOLO-NAS goes a step further with Quantization-Aware NAS.
- What's Quantization? It's a process to make models smaller and faster by using lower-precision numbers (like 8-bit integers instead of 32-bit floats). This is great for running models on less powerful devices (like phones or edge devices) or for speeding things up on powerful hardware.
- The Problem: Simply training a model and then quantizing it (Post-Training Quantization or PTQ) often hurts its accuracy.
- YOLO-NAS's Solution: The NAS process used to design YOLO-NAS knew the model would eventually be quantized. It specifically searched for architectures that would perform well after quantization. It optimized the design with quantization in mind from the beginning!
This clever Quantization-Aware NAS helps YOLO-NAS achieve great performance even when running in a super-efficient quantized mode (like INT8).
Quantization
It also incorporates specific building blocks designed to work well with quantization.
Benefits of New Architecture
Performance Metrics of YOLO-NAS
The smart design choices behind YOLO-NAS translate into real-world advantages:
- Top-Notch Accuracy: Thanks to the NAS process finding optimal structures, YOLO-NAS delivers state-of-the-art accuracy (measured by metrics like mAP) on standard object detection benchmarks like COCO. It often pushes the boundaries of what's possible at a given model size.
- Impressive Speed: The architecture is highly optimized for efficiency. Especially when quantized (using its INT8 version), YOLO-NAS runs very fast, achieving low latency. This is crucial for real-time applications.
- Better Accuracy/Latency Trade-off: This is a key benefit. YOLO-NAS often gives you more accuracy for the same speed, or more speed for the same accuracy, compared to previous models. It hits a sweet spot in performance.
- Quantization-Friendly: As mentioned, it was designed for quantization. This means the accuracy drop when moving to faster INT8 versions is often much smaller than with models that weren't designed this way.
- Scalability: YOLO-NAS comes in different sizes (e.g., Small, Medium, Large - yolo_nas_s, yolo_nas_m, yolo_nas_l), allowing you to choose the best balance of speed and accuracy for your specific needs and hardware.
Implementing YOLO-NAS
To perform object detection using YOLO-NAS in your local machine, you can use it using Ultralytics libraries. Also, import super_gradients as it is required for YOLO-NAS, but it will only work in versions lower than 3.11.
!pip install ultralytics super_gradients opencv-python
# Important Note: Use python<=3.11 as above python version donot support super_gradients < code>
from ultralytics import NAS
import matplotlib.pyplot as plt
import cv2
# Load a COCO-pretrained YOLO-NAS-l model
NAS = NAS("yolo_nas_l.pt")
YOLO_NAS = NAS("path/to/image")
plt.figure(figsize=(10, 10)) # set figure size
plt.title("YOLO-NAS OBJECT DETECTION") # set title
plt.axis('off') # hide axes
plt.imshow(cv2.cvtColor(YOLO_NAS[0].plot(), cv2.COLOR_BGR2RGB))
Comparison with YOLOv8 in the real world
YOLOv8 vs YOLO-NAS
Yolo-NAS detection confidence is higher than Yolov8 when detecting objects, which means it has a better understanding of objects.
YOLOv8 vs YOLO-NAS
Both Yolov8 and Yolo-NAS performed well in detecting small and multiple overlapping objects.
YOLOv8 vs YOLO-NAS
Here, Yolov8 failed at detecting the right number of objects, whereas Yolo-NAS succeeded.
YOLOv8 vs YOLO-NAS
Both Models failed in detecting overlapping objects.
Conclusion
YOLO-NAS represents a significant step in the evolution of object detection.
By leveraging Neural Architecture Search with a specific focus on quantization, it delivers an impressive blend of accuracy and speed.
Its ability to maintain high performance even in efficient INT8 mode makes it particularly attractive for real-world deployment on diverse hardware.
Whether you're building applications for edge devices, cloud services, or robotics, YOLO-NAS is definitely a model worth exploring.
FAQ
What's unique about YOLO-NAS compared to traditional YOLO models?
YOLO-NAS uses Neural Architecture Search (NAS), particularly Quantization-Aware NAS, to automatically design highly efficient architectures, unlike the manual design of many older YOLOs.
What are the main benefits of using YOLO-NAS?
YOLO-NAS offers a superior accuracy-latency trade-off, especially for INT8 quantized models, thanks to its NAS-optimized and quantization-aware design, leading to faster and more accurate detections.
How can I start using YOLO-NAS?
You can easily use pre-trained YOLO-NAS models or fine-tune them using Deci AI's super-gradients library with just a few lines of Python code.