Guide to Object Detection with YOLO-NAS

Last Updated on 27/11/2025 by Eran Feit

YOLO NAS object detection is all about combining modern deep learning with real-world practicality. At its core, YOLO NAS is a family of object detection models designed to spot and locate multiple objects in an image or video frame in real time. Instead of scanning an image piece by piece, the model “looks” at the entire image once and directly predicts bounding boxes and class labels, making it both fast and efficient.

What makes YOLO NAS stand out is the way its architecture was created. Instead of manually designing every block, it was generated using Neural Architecture Search (NAS), an automated process that explores thousands of model variations and selects the best balance between speed and accuracy. This leads to a detector that can outperform many earlier YOLO versions while still being lightweight enough for production workloads.

From a practical perspective, YOLO NAS object detection is ideal when you want good accuracy without sacrificing latency: tracking people in a store, monitoring vehicles on a road, analyzing sports footage, or scanning products on a conveyor belt. In these scenarios, every millisecond matters, and the model’s design is tuned to deliver predictions quickly even on modest GPUs.

For developers working in Python, YOLO NAS integrates smoothly into typical computer vision workflows. You load a pretrained model (often trained on COCO or similar datasets), pass in an image using libraries like OpenCV, and receive predictions that include bounding boxes, class IDs, and confidence scores. From there, you can visualize detections, filter them by class or confidence, and plug them into downstream analytics or business logic.

Getting comfortable with YOLO NAS object detection

When starting with YOLO NAS object detection, it helps to think about three key parts: the backbone, the neck, and the head. The backbone is responsible for extracting visual features from the raw image, such as edges, textures, and shapes. The neck aggregates these features at different scales so that small objects and large objects are both represented clearly. Finally, the head takes those aggregated features and turns them into predictions: bounding boxes, object classes, and confidence scores.

The target of YOLO NAS object detection is to detect many different object categories in a single forward pass, while staying fast enough for real-time applications. A single image might contain people, cars, bags, signs, or other everyday objects. YOLO NAS processes that image once and outputs a list of detections, each with coordinates and labels. Because the model was optimized via NAS, its layers and channels have been carefully tuned to squeeze out as much performance as possible from typical hardware setups.

At a high level, the workflow is straightforward. You take an input image, resize and normalize it, and feed it into the model. YOLO NAS outputs raw tensors that represent potential bounding boxes and their associated probabilities. Post-processing steps such as non-maximum suppression (NMS) then clean up overlapping predictions so that each object is represented by a single, high-confidence box. The result is an annotated image where each detected object is clearly framed and labeled.

In practical Python projects, these steps are wrapped in user-friendly APIs provided by libraries like SuperGradients. You might call a predict method on a pretrained YOLO NAS model and receive an object that already includes bounding boxes, labels, and confidence scores. With a few lines of OpenCV code, you can draw rectangles and class names on the image, save it to disk, or display it in a window. This makes YOLO NAS object detection accessible not only to deep learning experts, but also to beginners who want to quickly get useful results from real images and videos.

YOLO NAS Architecture

The YOLO-NAS architecture is built around the idea of combining high accuracy with real-time performance, using techniques like attention mechanisms, quantization-aware blocks, and reparametrization at inference time. These design choices help the model detect objects of different sizes and complexities more effectively than many traditional detectors. At the core of the network are three main components—backbone, neck, and head—each optimized using Neural Architecture Search (NAS) to work together as a unified object detection system.

The backbone is responsible for extracting meaningful features from the input image. It consists of a series of convolutional layers and custom blocks carefully tuned to capture both low-level features like edges and textures, and high-level features such as shapes and object parts. This rich feature representation is what allows the model to understand complex scenes instead of just isolated pixels.

The neck acts as a bridge between the backbone and the detection head, focusing on aggregating and enhancing features across multiple scales. YOLO-NAS uses a feature pyramid network (FPN) style neck with cross-stage partial connections and adaptive feature fusion. This structure enables information to flow efficiently between different depths of the network, helping the model handle small, medium, and large objects within the same image. By combining features from several levels, the neck provides a strong multi-scale representation that is crucial for robust object detection.

The head is where final detections are produced. YOLO-NAS uses a multi-scale detection head that improves on earlier YOLO variants by introducing an adaptive anchor-free design, multi-level feature fusion, and efficient channel attention. Instead of relying on fixed anchor boxes, the anchor-free approach lets the model dynamically learn how to place bounding boxes, which increases flexibility and precision. The head fuses features from different neck levels and applies lightweight channel attention to focus on the most relevant information, resulting in accurate and efficient predictions even under tight latency and hardware constraints.

Potential Use Cases of YOLO NAS

YOLO-NAS is designed to be a general-purpose, production-ready detector, making it suitable for a wide range of real-world tasks where both speed and accuracy matter. A typical workflow starts with a pretrained YOLO-NAS model, often trained on datasets like COCO or Objects365, and fine-tunes it on a domain-specific dataset using transfer learning. This allows organizations to adapt the model to their own classes and environments with a relatively small amount of labeled data while still benefiting from the strong representations learned during pretraining. Quantization-friendly building blocks make it easier to deploy the model in an optimized INT8 form, reducing memory and compute costs without heavily sacrificing accuracy.

In surveillance and security, YOLO-NAS can power smart cameras that detect people, vehicles, or suspicious activity in real time. Instead of a human constantly watching the video feed, the model can automatically flag events like intrusions, loitering, or objects left behind. Its robustness to clutter and occlusion helps it maintain performance in crowded or messy scenes, which are common in public spaces and industrial environments.

In autonomous driving and intelligent transportation, YOLO-NAS can help vehicles detect pedestrians, cyclists, cars, and obstacles with low latency. This quick perception is essential for decision-making tasks such as braking, lane changes, and collision avoidance. The model’s efficiency and ability to run in quantized form make it a compelling option for embedded hardware in cars, drones, and delivery robots, where compute and power budgets are limited.

Beyond these, YOLO-NAS is also applicable in medical imaging and retail. In medical scenarios, it can assist in detecting anomalies like tumors or lesions in X-rays, CT scans, or MRIs, serving as a second pair of eyes for radiologists. In retail, it can track products on shelves, monitor stock levels, or analyze customer behavior, enabling automated checkout systems or in-store analytics. Across all these domains, the combination of NAS-optimized architecture, quantization readiness, and strong accuracy–latency trade-offs makes YOLO-NAS a versatile foundation for modern object detection systems.

Getting hands-on with the YOLO-NAS object detection code

In this tutorial, the goal of the code is simple and practical: set up a clean Python environment, load a pretrained YOLO-NAS model, and run YOLO NAS object detection on a single image from start to finish. You begin by creating and activating a dedicated conda environment, then installing the correct PyTorch, CUDA, and super_gradients versions. This makes sure everything is stable and ready for GPU-accelerated inference, so you don’t fight with dependency issues while you’re trying to learn object detection.

Once the environment is ready, the code moves on to loading the YOLO-NAS-M model with pretrained COCO weights. This means you immediately have a detector that already knows how to recognize common objects like people, cars, dogs, and many other categories. You don’t train anything here; instead, you focus on understanding how to pass an image into the model and interpret the predictions it returns. This is the perfect starting point if you just want to see YOLO-NAS working on your own images.

The next part of the script uses OpenCV to read an image from disk, convert its color space, and send it through the model’s predict function. The output contains bounding boxes, labels, and confidence scores for each detected object. By iterating over these predictions, the code builds readable labels such as “person 0.98” or “dog 0.95” and prepares everything needed to visualize the detections on top of the original image.

Finally, the code shows how to draw the results and export them. With a few OpenCV calls, you draw colored rectangles around each object, put the label text above the boxes, convert the image back to the right color format, save it to disk, and display it in a window. The end result is a complete, minimal pipeline: from environment setup, through YOLO NAS object detection, all the way to a ready-to-share output image that clearly demonstrates what the model has found.

Link to the video tutorial : https://youtu.be/ptyt3Yf5Tt8

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Link for Medium users : https://medium.com/@feitgemel/guide-to-object-detection-with-yolo-nas-7f7db3c3856a

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YOLO-NAS object detection :

YOLO NAS object detection is all about getting a powerful, modern detector running in as few lines of Python as possible.
Instead of training a huge network from scratch, you plug into YOLO-NAS – a Neural Architecture Search–designed model that’s been optimized for accuracy, speed, and quantization-friendliness, and exposed via the SuperGradients library.

In this tutorial-style post, we’ll walk from a fresh Conda environment to a working YOLO NAS object detection script.
You’ll install PyTorch with CUDA support, bring in super_gradients, load a pretrained YOLO-NAS-M model, and run inference on a single image.

The code is broken into three parts so you can copy, paste, and understand each step: setting up the environment, running a minimal prediction, and then drawing your own bounding boxes with OpenCV.
By the end, you’ll have a small but complete YOLO NAS object detection pipeline you can reuse for demos, experiments, or as the starting point for production projects.

Setting up a clean YOLO-NAS environment with Conda

Before you can run YOLO NAS object detection, you need a stable Python environment with compatible versions of Python, PyTorch, CUDA, and SuperGradients.
This first part focuses entirely on that “plumbing”: creating a Conda environment, checking your CUDA version, installing GPU-enabled PyTorch, and finally installing super_gradients, which ships the YOLO-NAS models.

Once this block is working, you can forget about drivers and dependencies and focus on the actual object detection code.

### Create a new Conda environment named "Yolo-nas1" with Python 3.8.
conda create --name Yolo-nas1 python=3.8

### Activate the Conda environment so all remaining commands run inside it.
conda activate Yolo-nas1

### Check that the NVIDIA CUDA compiler is installed and see its version.
nvcc --version

### Install PyTorch 2.1.1, TorchVision 0.16.1, Torchaudio 2.1.1 and CUDA 11.8 build from the official channels.
conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia

### Install the SuperGradients library that provides ready-to-use YOLO-NAS models.
pip install super_gradients

After running these commands, you have a dedicated environment where YOLO NAS object detection can run with GPU acceleration.
If something fails here (CUDA mismatch, missing driver, etc.), fix it now — once this foundation is stable, the rest of the tutorial is usually smooth.

Loading YOLO-NAS and running your first prediction

Now that the environment is ready, it’s time to actually use YOLO-NAS.
In this part, you’ll import OpenCV and SuperGradients, load the YOLO_NAS_M model with COCO weights, read an image, and run a single YOLO NAS object detection pass on it.

The goal here is simple: verify that the model loads correctly and that model.predict() returns a valid result object.

Here is the test image :

### Import OpenCV for image loading, color conversion, drawing, and display.
import cv2

### Import the SuperGradients models module which knows how to load YOLO-NAS.
from super_gradients.training import models

### Import the Models enum so we can refer to YOLO_NAS_M by name.
from super_gradients.common.object_names import Models

### Load the YOLO-NAS-M model with COCO pretrained weights for general object detection.
model = models.get(Models.YOLO_NAS_M, pretrained_weights="coco")

### Read the input image from disk using OpenCV in BGR format.
img_bgr = cv2.imread("Best-Object-Detection-models/Yolo-Nas/Simple-Object-Detection-using-Yolo-Nas/haverim.jpg")

### Convert the BGR image to RGB, which is what most deep-learning models expect.
img = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2RGB)

### Run YOLO NAS object detection on the RGB image with a 0.6 confidence threshold.
results = model.predict(img, conf=0.6)

### Print the raw results object so you can inspect its structure in the console.
print(results)

### Use the built-in visualization helper to show detections in a pop-up window.
results.show()

With just these lines, you already have YOLO NAS object detection working end-to-end: an image goes in, and a detection result comes back.
The SuperGradients API hides a lot of complexity behind models.get(...) and model.predict(...), so you can focus on what the model sees rather than how it’s implemented.

Drawing YOLO-NAS detections manually with OpenCV

While results.show() is convenient, in real projects you often need full control over the detections: access to the bounding boxes, labels, and confidence scores so you can draw them yourself, log them, or feed them into downstream logic.

In this final code part, you’ll unpack the YOLO NAS object detection predictions, loop over each detection, draw rectangles and labels with OpenCV, save the result to disk, and display it in an OpenCV window.

### Extract bounding boxes in (x1, y1, x2, y2) format from the prediction object.
bboxes = results.prediction.bboxes_xyxy

### Get the numeric class labels for each detection.
labels = results.prediction.labels

### Get the confidence score for each detection.
confidences = results.prediction.confidence

### Read the list of all class names known by the model.
class_names = results.class_names

### Loop over each detection and draw it on the image.
for bbox, label, confidence in zip(bboxes, labels, confidences):
    ### Convert the bounding box coordinates from tensors/floats to plain integers.
    x1, y1, x2, y2 = map(int, bbox)

    ### Build a label string like "person 0.98" with the class name and confidence.
    label_text = f"{class_names[label]} {confidence:.2f}"

    ### Draw a rectangle around the detected object in red.
    cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)

    ### Put the label text slightly above the top-left corner of the bounding box.
    cv2.putText(img, label_text, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

### Convert the annotated image back to BGR so OpenCV can display and save it correctly.
output_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

### Save the annotated image as a PNG file on disk.
cv2.imwrite("c:/temp/detected.png", output_img)

### Show the annotated image in a resizable OpenCV window.
cv2.imshow("Detected image", output_img)

### Wait for a key press so the window does not close immediately.
cv2.waitKey(0)

### Close all OpenCV windows after the key press.
cv2.destroyAllWindows()

At this point, you’ve built a complete YOLO NAS object detection pipeline: environment setup, model loading, inference, and custom visualization.
You can swap the input image path, tune the confidence threshold, or later extend the loop to handle video frames, webcams, or additional post-processing.

FAQ: YOLO NAS object detection tutorial

Wrapping up this YOLO-NAS object detection example

You’ve just walked through a complete YOLO NAS object detection pipeline, from a blank Conda environment to a script that draws labeled boxes on your images.
The core idea is simple: let SuperGradients handle the heavy deep-learning details while you focus on images in and predictions out.

From here, you can grow this minimal example in many directions.
You might switch to YOLO_NAS_S for faster inference, or YOLO_NAS_L for maximum accuracy.
You can swap the input for video streams, add class filtering logic, or log detections into a database for analytics.

The main takeaway is that modern object detection no longer requires pages of boilerplate.
With a handful of carefully chosen lines, you can build a compact YOLO NAS object detection pipeline that is easy to read, easy to debug, and easy to extend into your own projects and tutorials.

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