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YOLO-NAS

Overview

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.

Model example image 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:

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 (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 mode. For using YOLO-NAS with additional modes see Val and Export. YOLO-NAS on the ultralytics package does not support training.

PyTorch pretrained *.pt models files can be passed to the NAS() class to create a model instance in 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 commands are available to directly run the models:

# 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 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 Object Detection
YOLO-NAS-m yolo_nas_m.pt Object Detection
YOLO-NAS-l yolo_nas_l.pt Object Detection

Citations and Acknowledgements

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

@misc{supergradients,
      doi = {10.5281/ZENODO.7789328},
      url = {https://zenodo.org/record/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 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.

Keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, SuperGradients, pre-trained models, quantization-friendly basic block, advanced training schemes, post-training quantization, AutoNAC optimization, COCO, Objects365, Roboflow 100



Created 2023-11-12, Updated 2024-01-14
Authors: glenn-jocher (7)

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