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.
Developed by Deci AI, YOLO-NAS is a groundbreaking object detection foundational model. It is the product of advanced Neural Architecture Search technology, meticulously designed to address the limitations of previous YOLO models. With significant improvements in quantization support and accuracy-latency trade-offs, YOLO-NAS represents a major leap in object detection.
 **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.
- **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:
Each model variant is designed to offer a balance between Mean Average Precision (mAP) and latency, helping you optimize your object detection tasks for both performance and speed.
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.
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.
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:
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.
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}},
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 community. We believe YOLO-NAS, with its innovative architecture and superior object detection capabilities, will become a critical tool for developers and researchers alike.
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.
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:
- **Quantization-Friendly Basic Block:** Enhanced architecture that improves model performance with minimal 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.
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.
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:
