Modern Computer Vision & Deep Learning with Python & PyTorch

سرفصل های دوره

Computer Vision with Python using Deep Learning for Classification, Instance & Semantic Segmentation, & Object Detection

1. Introduction

1. Introduction to Course

2. What is Computer Vision & its Applications

1. Introduction to Computer Vision and its Real-world Applications

2. Major Computer Vision Tasks

3. Deep Convolutional Neural Networks (CNN) for Computer Vision

1. Introduction to Convolutional Neural Networks (CNN)

4. Setting-up Google Colab for Writing Python Code

1. Introduction to Google Colab for Python Coding

2. Connect Google Colab with Google Drive

5. Image Classification Task of Computer Vision

1. Introduction to Single and Multi-label Image Classification

6. Pretrained Models for Single and Multi-Label Image Classification

1. Introduction to Pretrained Models

2. Deep Learning ResNet and AlexNet Architectures

3. Access Data from Google Drive to Colab

4. Data Preprocessing for Image Classification

5. Single-Label Image Classification using ResNet and AlexNet PreTrained Models

6.1 Lecture 2 - Resources Single Label Classification.zip

6. Single Label Classification Python and Pytorch Code.html

7. Multi-Label Image Classification using Deep Learning Models

8.1 Lecture 2 - Resources Multi Label Classification.zip

8. Multi-Label Classification Python and PyTorch Code.html

7. Transfer Learning for Image Classification

1. Introduction to Transfer Learning

2. Dataset, Data Augmentation, and Dataloaders

3.1 Classification Dataset.zip

3. Dataset for Classification.html

4. FineTuning Deep ResNet Model

5. HyperParameteres Optimization for Model

6. Training Deep ResNet Model

7. Fixed Feature Extractraction using ResNet

8. Model Optimization, Training and Results Visualization

9.1 Code for Transfer Learning by FineTuning and Model Feature Extractor.zip

9. Complete Python Code for Transfer Learning and Dataset.html

8. Semantic Segmentation Task Of Computer Vision

1. Introduction to Semantic Image Segmentation

2. Semantic Segmentation Real-World Applications

9. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)

1. Pyramid Scene Parsing Network (PSPNet) For Segmentation

2. UNet Architecture For Segmentation

3. Pyramid Attention Network (PAN)

4. Multi-Task Contextual Network (MTCNet)

10. Segmentation Datasets, Annotations, Data Augmentation & Data Loading

1. Datasets for Semantic Segmentation

2. Data Annotations Tool for Semantic Segmentation

3. Data Loading with PyTorch Customized Dataset Class

4. Data Loading for Segmentation with Python and PyTorch Code.html

5. Data Augmentation using Albumentations with Different Transformations

6. Augmentation Python Code.html

7. Learn To Implement Data Loaders In Pytorch

11. Performance Metrics (IOU) For Segmentation Models Evaluation

1. Performance Metrics (IOU, Pixel Accuracy, Precision, Recall, Fscore)

2. Code (Python and PyTorch).html

12. Encoders and Decoders For Segmentation In PyTorch

1. Transfer Learning And Pretrained Deep Resnet Architecture

2. Encoders for Segmentation with PyTorch Liberary

3. Decoders for Segmentation in PyTorch Liberary

13. Implementation, Optimization and Training Of Segmentation Models

1. Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, and UNet++)

2. Segmentation Models Code with Python.html

3. Learn To Optimize Hyperparameters For Segmentation Models

4. Model Optimaztion Code (Python And PyTorch).html

5. Training of Segmentation Models

6. Model Training Code (Python And PyTorch).html

14. Test Models and Visualize Segmentation Results

1. Test Models and Calculate IOU,Pixel Accuracy,Fscore

2. Test Models and Calculate Performance Scores (Python Code).html

3. Visualize Segmentation Results and Generate RGB Segmented Map

4. Segmentation Results Visualization (Python Code).html

15. Complete Code and Dataset for Semantic Segmentation

1. Final Code Review

2.1 Lecture 2 - Final Code.zip

2.2 Lecture 3 - TrayDataset for Segmentation.zip

2. Complete Code and Dataset is Attached.html

16. Object Detection Task Of Computer Vision

1. Object Detection and its Applications

17. Deep Learning Architectures for Object Detection (R-CNN Family)

1. Deep Convolutional Neural Network (VGG, ResNet, GoogleNet)

2. RCNN Deep Learning Architectures for Object Detection

3. Fast RCNN Deep Learning Architectures for Object Detection

4. Faster RCNN Deep Learning Architectures for Object Detection

18. Detectron2 for Ojbect Detection

1. Detectron2 for Ojbect Detection with PyTorch

2. Perform Object Detection using Detectron2 Pretrained Models

3.1 object detection with detctron2.zip

3. Python and PyTorch Code.html

19. Training, Evaluating and Visualizing Object Detection on Custom Dataset

1. Custom Dataset for Object Detection

2.1 balloon.zip

2. Dataset for Object Detection.html

3. Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset

4.1 object detection on custom dataset.zip

4. Python and PyTorch Code.html

20. Complete Code and Custom Dataset for Object Detection

1.1 balloon.zip

1.2 Python and PyTorch Code.zip

1. Resources Code and Custom Dataset for Object Detection.html

21. Instance Segmentation Task of Computer Vision

1. What is Instance Segmentation

22. Mask RCNN for Instance Segmentation

1. Mask RCNN for Instance Segmentation

23. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset

1. Train, Evaluate Instance Segmentation Model & Visualizing Results on Custom Data

24. Complete Code and Custom Dataset for Instance Segmentation

1.1 balloon.zip

1.2 Instance Segmentation on Custom Dataset.zip

1. Resources Complete Code and Custom Dataset for Instance Segmentation.html

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تولید کننده: Udemy