AI Images Map

Version 1.1 (12.6 KB) by Fred Liu
AI and image self learning map
11 Downloads
Updated 15 Dec 2023

AI_Images_Map

AI and image learning map

Deep Learning

MATLAB Integration

MATLAB APPs

Image Processing & Computer Vision

Code Project


Deep Learning Image Classification

當你初步踏入深度學習領域時,以下這幾個影片與程式碼專案可以成為你學習的起點,幫助你快速了解深度學習的基本概念和應用:

1.在MATLAB中安裝Pretrained Deep Learning Mode:這個影片會向你介紹在MATLAB中深度學習的模型該如何下載。

2.五行程式碼快速實現:在這個影片中,你會學到如何使用只有五行程式碼的方式來實現深度學習模型的推論(inference)過程,這對初學者來說是個很好的入門練習。

3.快速使用遷移式學習:遷移式學習是深度學習中常見且重要的技術,它允許你將一個已經訓練好的模型應用到新的任務上。這個影片會教你如何運用遷移式學習來加速你的深度學習應用。

4.使用圖形化介面不用寫Code完成深度學習:Deep Network Designer(深度學習工具)提供圖形化的介面,讓你無需寫程式碼也能建立和訓練深度學習模型。這個影片會帶你瞭解如何使用這些工具來加速模型開發。

When you are just starting out in the field of deep learning, the following videos and code projects can serve as your starting point to help you quickly grasp the basic concepts and applications of deep learning:

1.Installing Pretrained Deep Learning Models in MATLAB: This video will introduce you to how you can download deep learning models in MATLAB.

2.Five-Liner Code for Quick Implementation: In this video, you will learn how to implement the inference process of a deep learning model using only five lines of code, which is an excellent introductory exercise for beginners.

3.Quick Introduction to Transfer Learning: Transfer learning is a common and essential technique in deep learning that allows you to apply a pre-trained model to a new task. This video will teach you how to use transfer learning to speed up your deep learning applications.

4.Completing Deep Learning Tasks Without Writing Code Using a Graphical Interface: Deep Network Designer (a deep learning tool) provides a graphical interface that allows you to build and train deep learning models without writing code. This video will guide you on how to use these tools to accelerate model development.

深度學習初始介紹:

Youtube影片標題 GiuHub專案
在MATLAB中安裝Pretrained Deep Learning Mode @DeepLearning_Classification
使用五行程式碼,快速在MATLAB中使用深度學習模型 @DeepLearning_Classification
不用寫code,快速在MATLAB中使用Transfer Learning App,來建立屬於你的深度學習模型! @DeepLearning_Classification
Deep Network Designer(開發深度學習架構,並且進行訓練) @DeepLearning_Classification

如果透過上方已經了解初步概念與已經知道怎麼下載模型時,可以透過以下深度學習影像入門,完成單張影像的分類、多張影像的分類、自己從零開始訓練一個分類模型、 使用遷移式學習來快速訓練一個新模型。

If you have already gained a preliminary understanding from the above and know how to download models, you can proceed with the following image-based introduction to deep learning. This will allow you to accomplish tasks such as single image classification, multiple image classification, training a classification model from scratch, and utilizing transfer learning to rapidly train a new model.

深度學習影像入門:分類與遷移式學習

Youtube影片標題 GiuHub專案
MATLAB深度學習之一(Classification) @DeepLearning_Classification
MATLAB深度學習之二(Classification) @DeepLearning_Classification
MATLAB深度學習之三(Classification) @DeepLearning_Classification
MATLAB深度學習之四(上)(Transfer Learning) @DeepLearning_Classification
MATLAB深度學習之四(中)(Transfer Learning) @DeepLearning_Classification
MATLAB深度學習之四(下)(Transfer Learning) @DeepLearning_Classification

將已經訓練好的模型可視化,特別是產生模型的熱圖,可以幫助我們了解模型在進行分類預測時,對於影像中不同區域的關注程度,進而瞭解模型做出分類結果的依據, 此章節中會介紹幾種MATLAB中支援的可視化模型。

Visualizing a pre-trained model, especially generating heatmaps, can help us understand the model's attention to different regions in an image during classification prediction. This allows us to gain insights into the basis of the model's classification decisions. In this chapter, several visualization techniques supported in MATLAB will be introduced

深度學習影像進階:分類可視化

Youtube影片標題 GiuHub專案
MATLAB深度學習之九(1)深度學習分類可視化:什麼是模型可視化 @DeepLearning_Classification
MATLAB深度學習之九(2)深度學習分類可視化:Deep Dream @DeepLearning_Classification
MATLAB深度學習之九(3)深度學習分類可視化:GradCam @DeepLearning_Classification
MATLAB深度學習之九(4)深度學習分類可視化:Gradient Attribution @DeepLearning_Classification
MATLAB深度學習之九(5)深度學習分類可視化:LIME @DeepLearning_Classification
MATLAB深度學習之九(6)深度學習分類可視化:Occlusion @DeepLearning_Classification

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Deep Learning Image Object Detection

結束上段的深度學習分類,此章節會進入深度學習物件偵測的領域,在此段落會介紹如何使用MATLAB中的各種物件偵測模型進行辨識, 從標記影像、訓練模型、透過雲端來進行訓練最後實現並且進行加速。

Ending the previous section on deep learning classification, this chapter will delve into the field of deep learning object detection. In this paragraph, we will introduce how to use various object detection models available in MATLAB for recognition tasks. This includes steps such as annotating images, training the model, using cloud resources for training, and finally, implementing and accelerating the object detection process.

深度學習影像進階:物件偵測:

Youtube影片標題 GiuHub專案
MATLAB深度學習之五(1)(進階標記方式) @RabbitDetect
MATLAB深度學習之五(2)自動標記:YOLOX @RabbitDetect
MATLAB深度學習之六(1)RabbitDetect物件偵測篇:資料庫介紹 @RabbitDetect
MATLAB深度學習之六(2)RabbitDetect物件偵測篇:標記與如何載入其他格式標記檔案 @RabbitDetect
MATLAB深度學習之六(3)RabbitDetect物件偵測篇:FasterRCNN @RabbitDetect
MATLAB深度學習之六(4)RabbitDetect物件偵測篇:SSD @RabbitDetect
MATLAB深度學習之六(5)RabbitDetect物件偵測篇:YOLOv2 @RabbitDetect
MATLAB深度學習之六(6)RabbitDetect物件偵測篇:YOLOv3 @RabbitDetect
MATLAB深度學習之六(7)RabbitDetect物件偵測篇:YOLOv4 @RabbitDetect
MATLAB深度學習之六(8)RabbitDetect物件偵測篇:自定義YOLOv4架構:YOLOv4 @RabbitDetect
(no yet)MATLAB深度學習之六(9)RabbitDetect物件偵測篇:YOLOX @RabbitDetect

深度學習影像進階:物件偵測(實現與其他):

Youtube影片標題 GiuHub專案
使用五行程式碼,快速執行YOLOv4於MATLAB中:YOLOv4 @RabbitDetect
YOLOX Inference @RabbitDetect
AI雲端訓練方式(使用TWCC台智雲環境) @RabbitDetect
(No Code)Object Detection APP:YOLOv4 @RabbitDetect

深度學習影像進階:物件偵測(介紹論文與流程實驗數據文章):

Blog文章標題
YOLOX (MATLAB 2023b)

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Deep Learning Image Semantic Segmentation

結束上段的物件偵測後,此段落會進入到物件切割中的語意分割,在此所需要花費在標記的時間會更多一些,因為標記的東西開始不是個簡單 的方框,而會是一些複雜的形狀或是多邊形,因此在訓練上的效能所需也會更多一些,這邊目前是先介紹DeepLabv3+,日後再新增Unet的介紹。

Following the completion of the object detection section, this paragraph will delve into semantic segmentation in the domain of object segmentation. In semantic segmentation, the time required for annotation will be more extensive because annotations are no longer simple bounding boxes but complex shapes or polygons. Consequently, the performance demands for training will also increase. In this section, we will start by introducing DeepLabv3+, and later on, we will add an introduction to Unet as well.

深度學習影像進階:語意分割:

Youtube影片標題 GiuHub專案
MATLAB深度學習之七(1)RabbitDetect語意分割篇:資料標記 @RabbitDetect
MATLAB深度學習之七(2)RabbitDetect語意分割篇:標記與載入其他格式檔案 @RabbitDetect
MATLAB深度學習之七(3)RabbitDetect語意分割篇:DeepLabv3+(上篇) @RabbitDetect
MATLAB深度學習之七(3)RabbitDetect語意分割篇:DeepLabv3+(下篇) @RabbitDetect

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Deep Learning Image Instance Segmentation

結束語意分割後,此段落會進入物件切割中的實例分割,主要會介紹怎麼在MATLAB中使用MaskRCNN,並且從標記影像就開始做介紹到訓練 與實現被訓練好的模型。    After concluding the section on semantic segmentation, this paragraph will move on to instance segmentation in the domain of object segmentation. The main focus will be on how to use Mask R-CNN in MATLAB, covering the process from annotating images to training and implementing a trained model.

深度學習影像進階:實例分割:

Youtube影片標題 GiuHub專案
MATLAB深度學習之八(1)RabbitDetect實例分割篇:MaskRCNN Inference @RabbitDetect
MATLAB深度學習之八(2)RabbitDetect實例分割篇:資料標記:MaskRCNN Inference @RabbitDetect
MATLAB深度學習之八(3)RabbitDetect實例分割篇:標記與載入其他格式檔案 @RabbitDetect
MATLAB深度學習之八(4)RabbitDetect實例分割篇:訓練MaskRCNN @RabbitDetect
MATLAB深度學習之八(5)RabbitDetect實例分割篇:訓練MaskRCNN(訓練完後) @RabbitDetect
(no yet)MATLAB深度學習之八(6)RabbitDetect實例分割篇:SOLOv2 @RabbitDetect

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Deep Learning Inference

深度學習系列之十,以Demo與Inference還有五行Inference為主)

深度學習影像進階:Inference :

Youtube影片標題 GiuHub專案
深度學習系列之十(1) 物件偵測篇:YOLOX(2023b更新)檢測效果展示 @RabbitDetect
深度學習系列之十(2) 物件偵測篇:YOLOX(Demo Inference) @RabbitDetect
(no yet)深度學習系列之十(3) 實例分割篇:SOLOv2 (2023b更新)檢測效果展示 @RabbitDetect
(no yet)深度學習系列之十(4) 物件偵測篇:YOLOX (五行Inference)檢測效果展示 @RabbitDetect
(no yet)深度學習系列之十(5) 實例分割篇:SOLOv2 (五行Inference)檢測效果展示 @RabbitDetect

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Deep Learning Image Text Detection & OCR & Barcode

此區域會針對在工業界上常使用到的一些功能來做介紹,從影像上抓出文字區域、字元辨識(OCR)、與一維二維條碼辨識。

In this section, we will focus on introducing some commonly used functionalities in the industrial sector. We will cover topics such as extracting text regions from images, character recognition (OCR), and one-dimensional and two-dimensional barcode recognition.

深度學習影像專家:文字區域&字元(OCR)&條碼辨識:

Youtube影片標題 GiuHub專案
MATLAB深度學習之十一(1)文字辨識(Text Detection) @RabbitDetect
MATLAB深度學習之十一(2)New OCR(DL Base) @RabbitDetect
MATLAB深度學習之十一(3)Barcode檢測(一維與二維條碼) @RabbitDetect
Blog文章標題
AOI_Lab (MATLAB Visual Inspection )(上)

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Deep Learning Image Anomaly Detection

此篇章中會介紹如何在MATLAB中使用這最新的異常偵測模型,會介紹針對在影像上、訊號與最後數值上的異常偵測算法,尤其是在影像上的異常偵測模型, 都是2021~2023附近出的論文,算是蠻新穎且實用的演算法。

In this chapter, we will explore how to use the latest anomaly detection models in MATLAB. We will introduce anomaly detection algorithms for images, signals, and numerical data. Particularly, we will focus on anomaly detection models for images, which are based on cutting-edge research papers published around 2021 to 2023. These algorithms are novel and practical, making them highly relevant for various applications.

深度學習影像進階:異常偵測(Anomaly Detection):

Youtube影片標題 GiuHub專案
MATLAB深度學習之十二(1)異常偵測(Anomaly Detection) @RabbitDetect

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Deep Learning Exten

基於在深度學習的影像以外的領域的範例統整與介紹,以及深入在深度學習中各種進階操作方式。      深度學習擴充:DL_Exten:

Youtube影片標題 GiuHub專案
深度學習系列專案項目 - DL Exten @DL_Exten
Blog文章標題
AOI_Lab (MATLAB Visual Inspection )(下)

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深度學習影像進階:未來更新內容

10.1 深度學習進階(架構設計)@DL_Exten系列

10.2 深度學習進階(自定義層)@DL_Exten系列

10.3 深度學習進階(Auto Labeling)@DL_Exten系列

13.1 Image Captioning@DL_Exten系列

13.2 GAN(生成對抗網路)@DL_Exten系列

14.1 Verification@DL_Exten系列

15.1 DL>數值@DL_Exten系列

15.2 DL>訊號@DL_Exten系列

16.1 強化學習@RL_Lab系列

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MATLAB Integration with Python

MATLAB中寫Python,與TensorFlow,PyTorch,ONNX整合:

Youtube影片標題 GiuHub專案
MATLAB Integration(1) - 深度學習模型 TensorFlow/PyTorch/ONNX整合 @Python_MATLAB_Intergation
MATLAB Integration(2) - 在MATLAB中使用Python Code @Python_MATLAB_Intergation
MATLAB Integration(3) - 在Python中使用MATLAB @Python_MATLAB_Intergation
Blog文章標題
實現整合系列第一期:MATLAB中寫Python,與TensorFlow,PyTorch整合

MATLAB 與 Python 整合實現(中文圈最新最強教材):

以下為Tim所撰寫的MATLAB與Python整合相關的教材與教學,內容包含了在MATLAB與Python中的雙向溝通,以及實現多語言與環境的整合,例如Python與Simulink、如何MATLAB中執行Python後,在VS Code中debug python code等等.....

專案(教材&電子書)
MATLAB 與 Python 整合實現(create by Tim )

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Integration with Software(Compiler & Compiler SDK)

整體框架介紹:

Blog文章標題
MATLAB實現整合系列:框架介紹

Compiler & Compiler SDK:

Blog文章標題
MATLAB實現整合系列:Compiler
MATLAB實現整合系列:Compiler SDK(上)C#.NET

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MATLAB Integration with Hardware

MATLAB Integration With Hardware:

Youtube影片標題 GiuHub專案
MATLAB GPU Coder YOLO 口罩偵測實現於Jetson Nano MATLAB_Mask_Detection-with-Jetoson-Nano
Human Pose Estimation(with Jetson Nano) MATLAB_Mask_Detection-with-Jetoson-Nano
使用工具 硬體 GiuHub專案
GPU Coder Jetson Nano MATLAB_Mask_Detection-with-Jetoson-Nano
GPU Coder Jetson Nano Jetson_Nano_resnet50

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MATLAB Integration With Hardware:Future Update

1.Integration With ARM

2.Integration With FPGA

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MATLAB Image APPs

影像應用系列:

Youtube影片標題 系列
2023a更新!影像標記(Image Labeler) MATLAB三分鐘不用寫Code系列
2023a更新!影像分析(Image Region Analyzer) MATLAB三分鐘不用寫Code系列
2023a更新!色彩切割(2)相機擷取(Color Thresholder APP) MATLAB三分鐘不用寫Code系列
2023a更新!色彩切割(Color Thresholder APP) MATLAB三分鐘不用寫Code系列
2023a更新!影像切割(Image Segmenter) MATLAB三分鐘不用寫Code系列
色彩切割(Color Thresholder) MATLAB三分鐘不用寫Code系列
影像切割(Image Segmenter) MATLAB三分鐘不用寫Code系列
影像分析( Image_Region_Analyzer) MATLAB三分鐘不用寫Code系列
影像校正(Registration_Estimator) MATLAB三分鐘不用寫Code系列
影像標記(Image Labeler) MATLAB三分鐘不用寫Code系列
AOI連接相機影像截取工具(Image acquisition)] MATLAB三分鐘不用寫Code系列
(進階參數設定)AOI連接相機影像截取工具(Image Acquisition) MATLAB三分鐘不用寫Code系列
影像批次產生工具(Image Batch Processor) MATLAB三分鐘不用寫Code系列
高光譜影像(Hyperspectral Viewer) MATLAB三分鐘不用寫Code系列

生醫系列:

Youtube影片標題 系列
生醫影像檢視(Volume Viewer) MATLAB三分鐘不用寫Code系列
生醫影像切割(Volume Segmenter) MATLAB三分鐘不用寫Code系列
醫學影像標記工具(Medical Image Labeler) MATLAB三分鐘不用寫Code系列

Lidar光達:

Youtube影片標題 系列
點雲檢視工具(Lidar Viewer) MATLAB三分鐘不用寫Code系列
Lidar校正(Lidar Camera Calibrator) MATLAB三分鐘不用寫Code系列

相機校正:

Youtube影片標題 系列
單眼相機校正(Camera Calibrator) MATLAB三分鐘不用寫Code系列
雙眼相機校正(Stereo Camera Calibrator) MATLAB三分鐘不用寫Code系列
Lidar校正(Lidar Camera Calibrator) MATLAB三分鐘不用寫Code系列

標記(Labeler):

Youtube影片標題 系列
2023a更新!影像標記(Image Labeler) MATLAB三分鐘不用寫Code系列
影像標記(Image Labeler) MATLAB三分鐘不用寫Code系列
光達標記工具(Lidar Labeler) MATLAB三分鐘不用寫Code系列
醫學影像標記工具(Medical Image Labeler) MATLAB三分鐘不用寫Code系列

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MATLAB Deep Leanring APPs

Youtube影片標題 系列
強化學習(Reinforcement Learning) MATLAB三分鐘不用寫Code系列
2022a更新!深度學習(Deep Network Designer) MATLAB三分鐘不用寫Code系列
2022a更新!機器學習(Classification Learner) MATLAB三分鐘不用寫Code系列
深度學習超參數搜索(Experiment Manager) MATLAB三分鐘不用寫Code系列
GPU使用指南 MATLAB三分鐘不用寫Code系列

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Customized APPs

GiuHub專案 內容介紹
ObjectDetectionAPP 不用寫Code實現物件偵測演算法
AOI_Layout 不用寫Code實現影像演算法
Style_Transfer 遷移式風格轉換
Image_Captioning 影像文字輸出模型
Image_Inpainting 影像修補功能

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Image Processing & Computer Vision

Cite As

Fred Liu (2024). AI Images Map (https://github.com/MoonUsagi/AI_Images_Map/releases/tag/v1.1), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2023b
Compatible with any release
Platform Compatibility
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Version Published Release Notes
1.1

To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.