image_to_pixle_params_yoloSAM/ultralytics-main/docs/en/models/yolo-nas.md

---
comments: true
description: Discover YOLO-NAS by Deci AI - a state-of-the-art object detection model with quantization support. Explore features, pretrained models, and implementation examples.
keywords: YOLO-NAS, Deci AI, object detection, deep learning, Neural Architecture Search, Ultralytics, Python API, YOLO model, SuperGradients, pretrained models, quantization, AutoNAC
---

# YOLO-NAS

!!! note "Important Update"

    Please note that [Deci](https://www.linkedin.com/company/deciai/), the original creators of YOLO-NAS, have been acquired by NVIDIA. As a result, these models are no longer actively maintained by Deci. Ultralytics continues to support the usage of these models, but no further updates from the original team are expected.

## Overview

Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced [Neural Architecture Search](https://www.ultralytics.com/glossary/neural-architecture-search-nas) technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and [accuracy](https://www.ultralytics.com/glossary/accuracy)-latency trade-offs, YOLO-NAS represents a major leap in object detection.

![Model example image](https://github.com/ultralytics/docs/releases/download/0/yolo-nas-coco-map-metrics.avif) **Overview of YOLO-NAS.** YOLO-NAS employs quantization-aware blocks and selective quantization for optimal performance. The model, when converted to its INT8 quantized version, experiences a minimal precision drop, a significant improvement over other models. These advancements culminate in a superior architecture with unprecedented object detection capabilities and outstanding performance.

### Key Features

- **Quantization-Friendly Basic Block:** YOLO-NAS introduces a new basic block that is friendly to quantization, addressing one of the significant limitations of previous YOLO models.
- **Sophisticated Training and Quantization:** YOLO-NAS leverages advanced training schemes and post-training quantization to enhance performance.
- **AutoNAC Optimization and Pre-training:** YOLO-NAS utilizes AutoNAC optimization and is pre-trained on prominent datasets such as COCO, Objects365, and Roboflow 100. This pre-training makes it extremely suitable for downstream object detection tasks in production environments.

## Pre-trained Models

Experience the power of next-generation object detection with the pre-trained YOLO-NAS models provided by Ultralytics. These models are designed to deliver top-notch performance in terms of both speed and accuracy. Choose from a variety of options tailored to your specific needs:

!!! tip "Performance"

    === "Detection (COCO)"

        | Model            | mAP   | Latency (ms) |
        | ---------------- | ----- | ------------ |
        | YOLO-NAS S       | 47.5  | 3.21         |
        | YOLO-NAS M       | 51.55 | 5.85         |
        | YOLO-NAS L       | 52.22 | 7.87         |
        | YOLO-NAS S INT-8 | 47.03 | 2.36         |
        | YOLO-NAS M INT-8 | 51.0  | 3.78         |
        | YOLO-NAS L INT-8 | 52.1  | 4.78         |

Each model variant is designed to offer a balance between [Mean Average Precision](https://www.ultralytics.com/glossary/mean-average-precision-map) (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.

## Usage Examples

Ultralytics has made YOLO-NAS models easy to integrate into your Python applications via our `ultralytics` python package. The package provides a user-friendly Python API to streamline the process.

The following examples show how to use YOLO-NAS models with the `ultralytics` package for inference and validation:

### Inference and Validation Examples

In this example we validate YOLO-NAS-s on the COCO8 dataset.

!!! example

    This example provides simple inference and validation code for YOLO-NAS. For handling inference results see [Predict](../modes/predict.md) mode. For using YOLO-NAS with additional modes see [Val](../modes/val.md) and [Export](../modes/export.md). YOLO-NAS on the `ultralytics` package does not support training.

    === "Python"

        [PyTorch](https://www.ultralytics.com/glossary/pytorch) pretrained `*.pt` models files can be passed to the `NAS()` class to create a model instance in python:

        ```python
        from ultralytics import NAS

        # Load a COCO-pretrained YOLO-NAS-s model
        model = NAS("yolo_nas_s.pt")

        # Display model information (optional)
        model.info()

        # Validate the model on the COCO8 example dataset
        results = model.val(data="coco8.yaml")

        # Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
        results = model("path/to/bus.jpg")
        ```

    === "CLI"

        CLI commands are available to directly run the models:

        ```bash
        # Load a COCO-pretrained YOLO-NAS-s model and validate it's performance on the COCO8 example dataset
        yolo val model=yolo_nas_s.pt data=coco8.yaml

        # Load a COCO-pretrained YOLO-NAS-s model and run inference on the 'bus.jpg' image
        yolo predict model=yolo_nas_s.pt source=path/to/bus.jpg
        ```

## Supported Tasks and Modes

We offer three variants of the YOLO-NAS models: Small (s), Medium (m), and Large (l). Each variant is designed to cater to different computational and performance needs:

- **YOLO-NAS-s**: Optimized for environments where computational resources are limited but efficiency is key.
- **YOLO-NAS-m**: Offers a balanced approach, suitable for general-purpose [object detection](https://www.ultralytics.com/glossary/object-detection) with higher accuracy.
- **YOLO-NAS-l**: Tailored for scenarios requiring the highest accuracy, where computational resources are less of a constraint.

Below is a detailed overview of each model, including links to their pre-trained weights, the tasks they support, and their compatibility with different operating modes.

| Model Type | Pre-trained Weights                                                                           | Tasks Supported                        | Inference | Validation | Training | Export |
| ---------- | --------------------------------------------------------------------------------------------- | -------------------------------------- | --------- | ---------- | -------- | ------ |
| YOLO-NAS-s | [yolo_nas_s.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_s.pt) | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ❌       | ✅     |
| YOLO-NAS-m | [yolo_nas_m.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_m.pt) | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ❌       | ✅     |
| YOLO-NAS-l | [yolo_nas_l.pt](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_l.pt) | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ❌       | ✅     |

## Citations and Acknowledgements

If you employ YOLO-NAS in your research or development work, please cite SuperGradients:

!!! quote ""

    === "BibTeX"

        ```bibtex
        @misc{supergradients,
              doi = {10.5281/ZENODO.7789328},
              url = {https://zenodo.org/records/7789328},
              author = {Aharon,  Shay and {Louis-Dupont} and {Ofri Masad} and Yurkova,  Kate and {Lotem Fridman} and {Lkdci} and Khvedchenya,  Eugene and Rubin,  Ran and Bagrov,  Natan and Tymchenko,  Borys and Keren,  Tomer and Zhilko,  Alexander and {Eran-Deci}},
              title = {Super-Gradients},
              publisher = {GitHub},
              journal = {GitHub repository},
              year = {2021},
        }
        ```

We express our gratitude to Deci AI's [SuperGradients](https://github.com/Deci-AI/super-gradients/) team for their efforts in creating and maintaining this valuable resource for the [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.

## FAQ

### What is YOLO-NAS and how does it improve over previous YOLO models?

YOLO-NAS, developed by Deci AI, is a state-of-the-art object detection model leveraging advanced Neural Architecture Search (NAS) technology. It addresses the limitations of previous YOLO models by introducing features like quantization-friendly basic blocks and sophisticated training schemes. This results in significant improvements in performance, particularly in environments with limited computational resources. YOLO-NAS also supports quantization, maintaining high accuracy even when converted to its INT8 version, enhancing its suitability for production environments. For more details, see the [Overview](#overview) section.

### How can I integrate YOLO-NAS models into my Python application?

You can easily integrate YOLO-NAS models into your Python application using the `ultralytics` package. Here's a simple example of how to load a pre-trained YOLO-NAS model and perform inference:

```python
from ultralytics import NAS

# Load a COCO-pretrained YOLO-NAS-s model
model = NAS("yolo_nas_s.pt")

# Validate the model on the COCO8 example dataset
results = model.val(data="coco8.yaml")

# Run inference with the YOLO-NAS-s model on the 'bus.jpg' image
results = model("path/to/bus.jpg")
```

For more information, refer to the [Inference and Validation Examples](#inference-and-validation-examples).

### What are the key features of YOLO-NAS and why should I consider using it?

YOLO-NAS introduces several key features that make it a superior choice for object detection tasks:

- **Quantization-Friendly Basic Block:** Enhanced architecture that improves model performance with minimal [precision](https://www.ultralytics.com/glossary/precision) drop post quantization.
- **Sophisticated Training and Quantization:** Employs advanced training schemes and post-training quantization techniques.
- **AutoNAC Optimization and Pre-training:** Utilizes AutoNAC optimization and is pre-trained on prominent datasets like COCO, Objects365, and Roboflow 100.

These features contribute to its high accuracy, efficient performance, and suitability for deployment in production environments. Learn more in the [Key Features](#key-features) section.

### Which tasks and modes are supported by YOLO-NAS models?

YOLO-NAS models support various object detection tasks and modes such as inference, validation, and export. They do not support training. The supported models include YOLO-NAS-s, YOLO-NAS-m, and YOLO-NAS-l, each tailored to different computational capacities and performance needs. For a detailed overview, refer to the [Supported Tasks and Modes](#supported-tasks-and-modes) section.

### Are there pre-trained YOLO-NAS models available and how do I access them?

Yes, Ultralytics provides pre-trained YOLO-NAS models that you can access directly. These models are pre-trained on datasets like COCO, ensuring high performance in terms of both speed and accuracy. You can download these models using the links provided in the [Pre-trained Models](#pre-trained-models) section. Here are some examples:

- [YOLO-NAS-s](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_s.pt)
- [YOLO-NAS-m](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_m.pt)
- [YOLO-NAS-l](https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo_nas_l.pt)