https://github.com/matlab-deep-learning/pretrained-yolox-network-for-object-detection

YOLOX inference in MATLAB for Object Detection with yolox_s, yolox_m & yolox_l networks
https://github.com/matlab-deep-learning/pretrained-yolox-network-for-object-detection

computer-vision deep-learning image-processing matlab matlab-deep-learning object-detection pretrained-models yolo yolox-models

Last synced: 15 days ago
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YOLOX inference in MATLAB for Object Detection with yolox_s, yolox_m & yolox_l networks

• Host: GitHub
• URL: https://github.com/matlab-deep-learning/pretrained-yolox-network-for-object-detection
• Owner: matlab-deep-learning
• License: other
• Created: 2022-07-12T12:44:52.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-01T12:54:20.000Z (12 months ago)
• Last Synced: 2024-02-27T11:25:02.968Z (11 months ago)
• Topics: computer-vision, deep-learning, image-processing, matlab, matlab-deep-learning, object-detection, pretrained-models, yolo, yolox-models
• Homepage: https://www.mathworks.com/help/vision/ug/getting-started-with-object-detection-using-deep-learning.html
• Size: 283 KB
• Stars: 23
• Watchers: 6
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Security: SECURITY.md

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README

        
# Pretrained YOLOX Network For Object Detection

This repository provides multiple pretrained YOLOX [1] object detection networks for MATLAB®, trained on the COCO 2017[2] dataset. These object detectors can detect 80 different object categories including [person, car, traffic light, etc](/src/%2Bhelper/getCOCOClasess.m). [![Open in MATLAB Online](https://www.mathworks.com/images/responsive/global/open-in-matlab-online.svg)](https://matlab.mathworks.com/open/github/v1?repo=matlab-deep-learning/Pretrained-YOLOX-Network-For-Object-Detection)

**Creator**: MathWorks Development

## Requirements

- MATLAB® R2023b or later

- Deep Learning Toolbox™

- Computer Vision Toolbox™

- Computer Vision Toolbox™ Automated Visual Inspection Library

Note: Previous MATLAB® release users can use [this](https://github.com/matlab-deep-learning/Pretrained-YOLOX-Network-For-Object-Detection/tree/previous) branch to download the pretrained models.

## Getting Started

[Getting Started with YOLOX for Object Detection](https://in.mathworks.com/help/vision/ug/getting-started-with-yolox-object-detection.html)

### Detect Objects Using Pretrained YoloX

Use to code below to perform detection on an example image using the pretrained model.

Note: This functionality requires Deep Learning Toolbox™ and the Computer Vision Toolbox™ Automated Visual Inspection Library. You can install the Computer Vision Toolbox Automated Visual Inspection Library from Add-On Explorer. For more information about installing add-ons, see [Get and Manage Add-Ons](https://in.mathworks.com/help/matlab/matlab_env/get-add-ons.html).

```matlab

% Read test image.

img = imread(fullfile("data", "inputTeam.jpg"));

% Create a yoloxobjectdetector object to configure a pretrained YOLOX network with a CSP-DarkNet-53 backbone as the feature extractor.

detector = yoloxObjectDetector("small-coco");

% Perform detection using pretrained model.

[bboxes,scores,labels] = detect(detector,I);

% Visualize results.

annotations = string(labels) + ": " + (round(100*scores)) + "%";

img = insertObjectAnnotation(img, "rectangle", bboxes, annotations);

figure, imshow(img);

```

![Results](/data/results.jpg)

### Train YOLOX Network and Perform Transfer Learning

To train a YOLOX object detection network on a labeled data set, use the [trainYOLOXObjectDetector](https://in.mathworks.com/help/vision/ref/trainyoloxobjectdetector.html) function. You must specify the class names for the data set you use to train the network. Then, train an untrained or pretrained network by using the [trainYOLOXObjectDetector](https://in.mathworks.com/help/vision/ref/trainyoloxobjectdetector.html) function. The training function returns the trained network as a [yoloxObjectDetector](https://in.mathworks.com/help/vision/ref/yoloxobjectdetector.html) object.

To learn how to configure and train a YOLOX object detector for transfer learning to detect small objects, see the [Detect Defects on Printed Circuit Boards Using YOLOX Network](https://in.mathworks.com/help/vision/ug/detect-pcb-defects-using-yolox-deep-learning.html) example.

## Network Details

YOLOX is one of the best performing object detectors and is considered as an improvement to the existing YOLO variants such as YOLO v4, and YOLO v5.

![YOLOX architecture](/data/yolox_arch.png)

## References

[1] Ge, Zheng, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. "Yolox: Exceeding yolo series in 2021." arXiv preprint arXiv:2107.08430 (2021).

[2] Lin, T., et al. "Microsoft COCO: Common objects in context. arXiv 2014." arXiv preprint arXiv:1405.0312 (2014).

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