Udemy - Modern Computer Vision & Deep Learning with Python & PyTorch (12.2024)

磁力链接/BT种子名称

Udemy - Modern Computer Vision & Deep Learning with Python & PyTorch (12.2024)

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种子哈希：a430705579a5b77e519fceff3ad27cbcca297aa0
文件大小： 4.86G
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收录时间：2025-10-01
最近下载：2025-10-12

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文件列表

  • 09. Overview of YOLO Family for Object Detection/01. Overview of YOLO Family.mp4 277.8 MB
  • 05. 1. Image Classification Task of Computer Vision/02. Image Classification with Deep Convolutional Neural Networks using Python.mp4 241.1 MB
  • 20. Implementation, Optimization and Training Of Segmentation Models/01. Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, and UNet++).mp4 207.3 MB
  • 07. Transfer Learning for Image Classification/assets/Classification-Dataset.zip 184.8 MB
  • 25. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset/02. Train, Evaluate Instance Segmentation Model & Visualizing Results on Custom Data.mp4 182.5 MB
  • 04. Computer Vision and Deep Convolutional Neural Networks/03. Coding Convolutional Neural Network Architecture from Scratch.mp4 171.0 MB
  • 12. Detectron2 for Ojbect Detection/01. Detectron2 for Ojbect Detection with PyTorch.mp4 168.2 MB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/03. Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset.mp4 160.9 MB
  • 03. Deep Learning for Computer Vision/01. Basics of Deep Learning for Computer Vision.mp4 158.1 MB
  • 11. Overview of RCNN Family for Object Detection/01. Overview of RCNN Family for Object Detection.mp4 154.5 MB
  • 15. 3. Semantic Segmentation Task Of Computer Vision/02. Semantic Segmentation Real-World Applications.mp4 151.4 MB
  • 12. Detectron2 for Ojbect Detection/02. Perform Object Detection using Detectron2 Pretrained Models.mp4 143.7 MB
  • 04. Computer Vision and Deep Convolutional Neural Networks/06. Calculate Accuracy, Precision, Recall and Visualize Confusion Matrix.mp4 124.0 MB
  • 21. Test Models and Visualize Segmentation Results/01. Test Models and Calculate IOU,Pixel Accuracy,Fscore.mp4 119.0 MB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/04. Data Loading with PyTorch Customized Dataset Class.mp4 114.9 MB
  • 02. What is Computer Vision & its Applications/01. Introduction to Computer Vision and its Real-world Applications.mp4 114.0 MB
  • 10. Video Object Detection in Real-time/04. Testing YOLO8 on Videos and Images.mp4 112.7 MB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/01. Custom Dataset for Object Detection.mp4 92.7 MB
  • 25. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset/01. Custom Dataset for Instance Segmentation.mp4 90.3 MB
  • 21. Test Models and Visualize Segmentation Results/03. Visualize Segmentation Results and Generate RGB Segmented Map.mp4 90.1 MB
  • 10. Video Object Detection in Real-time/01. Vehicles Detection Custom Dataset.mp4 72.7 MB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/06. Data Augmentation using Albumentations with Different Transformations.mp4 67.4 MB
  • 18. Performance Metrics (IOU) For Segmentation Models Evaluation/01. Performance Metrics (IOU, Pixel Accuracy, Precision, Recall, Fscore).mp4 64.0 MB
  • 10. Video Object Detection in Real-time/02. Setting HyperParameters for YOLO8.mp4 61.3 MB
  • 27. Video Instance Segmentation/05. Training Video Instance Segmentation Model.mp4 60.1 MB
  • 27. Video Instance Segmentation/02. YOLO8 for Video Instance Segmentation.mp4 58.6 MB
  • 10. Video Object Detection in Real-time/05. Calculate Performance Metrics (Precision, Recall, Mean Average Precision mAP).mp4 57.0 MB
  • 04. Computer Vision and Deep Convolutional Neural Networks/01. Computer Vision using Convolutional Neural Networks (CNN).mp4 56.0 MB
  • 20. Implementation, Optimization and Training Of Segmentation Models/05. Training of Segmentation Models.mp4 55.4 MB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/02. Data Annotations Tool for Semantic Segmentation.mp4 52.8 MB
  • 07. Transfer Learning for Image Classification/04. FineTuning Deep ResNet Model.mp4 51.4 MB
  • 27. Video Instance Segmentation/07. Testing Segmentation Model on Videos.mp4 44.8 MB
  • 10. Video Object Detection in Real-time/03. Training YOLO8 on Vehicles Dataset.mp4 44.6 MB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/01. Datasets for Semantic Segmentation.mp4 43.4 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/07. Multi-Label Image Classification using Deep Learning Models.mp4 40.1 MB
  • 10. Video Object Detection in Real-time/assets/VehiclesDetection-Dataset.zip 39.9 MB
  • 27. Video Instance Segmentation/assets/TestVideos.zip 39.6 MB
  • 09. Overview of YOLO Family for Object Detection/03. YOLOv8 and its Architecture.mp4 39.3 MB
  • 04. Computer Vision and Deep Convolutional Neural Networks/05. Training Convolutional Neural Network from Scratch.mp4 38.7 MB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/assets/balloon.zip 38.7 MB
  • 14. Complete Code and Custom Dataset for Object Detection/assets/balloon.zip 38.7 MB
  • 26. Complete Code and Custom Dataset for Instance Segmentation/assets/balloon.zip 38.7 MB
  • 07. Transfer Learning for Image Classification/08. Model Optimization, Training and Results Visualization.mp4 37.7 MB
  • 27. Video Instance Segmentation/01. Intro to Video Instance Segmentation.mp4 37.0 MB
  • 19. Encoders and Decoders For Segmentation In PyTorch/01. Transfer Learning And Pretrained Deep Resnet Architecture.mp4 36.9 MB
  • 07. Transfer Learning for Image Classification/02. Dataset, Data Augmentation, and Dataloaders.mp4 36.7 MB
  • 04. Computer Vision and Deep Convolutional Neural Networks/04. Convolutional Neural Networks HyperParameters Optimization.mp4 36.6 MB
  • 15. 3. Semantic Segmentation Task Of Computer Vision/01. Semantic Segmentation Task Of Computer Vision with Pytorch and Python.mp4 34.6 MB
  • 10. Video Object Detection in Real-time/06. Export and Deploy the Model.mp4 33.1 MB
  • 04. Computer Vision and Deep Convolutional Neural Networks/02. Setting-up Google Colab for Writing Python Code.mp4 32.9 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/05. Single-Label Image Classification using ResNet and AlexNet PreTrained Models.mp4 32.5 MB
  • 23. 4. Instance Segmentation Task of Computer Vision/01. Instance Segmentation Task of Computer Vision with Python.mp4 32.4 MB
  • 27. Video Instance Segmentation/03. Custom Dataset for Instance Segmentation.mp4 30.3 MB
  • 08. 2. Object Detection Task Of Computer Vision/01. Object Detection Task Of Computer Vision with Python.mp4 29.4 MB
  • 27. Video Instance Segmentation/06. Testing Segmentation Model on Images.mp4 28.9 MB
  • 19. Encoders and Decoders For Segmentation In PyTorch/03. Decoders for Segmentation in PyTorch Liberary.mp4 28.0 MB
  • 07. Transfer Learning for Image Classification/05. HyperParameteres Optimization for Model.mp4 27.8 MB
  • 07. Transfer Learning for Image Classification/01. Introduction to Transfer Learning.mp4 26.7 MB
  • 01. Introduction to Course/01. Introduction to Computer Vision Course.mp4 25.8 MB
  • 10. Video Object Detection in Real-time/assets/TestVideo.zip 25.7 MB
  • 22. Complete Code and Dataset for Semantic Segmentation/01. Final Code Review.mp4 25.6 MB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/01. Pyramid Scene Parsing Network (PSPNet) For Segmentation.mp4 25.6 MB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/04. Multi-Task Contextual Network (MTCNet).mp4 25.0 MB
  • 27. Video Instance Segmentation/04. Setting-up Hyper Parameters for Model.mp4 24.7 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/01. Introduction to Pretrained Models.mp4 22.3 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/02. Deep Learning ResNet and AlexNet Architectures.mp4 22.0 MB
  • 19. Encoders and Decoders For Segmentation In PyTorch/02. Encoders for Segmentation with PyTorch Liberary.mp4 20.9 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/03. Access Data from Google Drive to Colab.mp4 20.5 MB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/assets/TrayDataset-for-Segmentation.zip 19.5 MB
  • 22. Complete Code and Dataset for Semantic Segmentation/assets/Lecture-3-TrayDataset-for-Segmentation.zip 19.5 MB
  • 14. Complete Code and Custom Dataset for Object Detection/assets/Python-and-PyTorch-Code.zip 18.9 MB
  • 27. Video Instance Segmentation/08. Deploy Video Segmentation Model.mp4 18.8 MB
  • 07. Transfer Learning for Image Classification/07. Fixed Feature Extractraction using ResNet.mp4 18.6 MB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/03. Pyramid Attention Network (PAN).mp4 18.5 MB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/08. Learn To Implement Data Loaders In Pytorch.mp4 17.8 MB
  • 24. Mask RCNN for Instance Segmentation/01. Mask RCNN for Instance Segmentation.mp4 17.6 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/04. Data Preprocessing for Image Classification.mp4 17.3 MB
  • 20. Implementation, Optimization and Training Of Segmentation Models/03. Learn To Optimize Hyperparameters For Segmentation Models.mp4 17.3 MB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/assets/Object-Detection-On-Custom-Dataset.ipynb 16.4 MB
  • 07. Transfer Learning for Image Classification/06. Training Deep ResNet Model.mp4 14.1 MB
  • 12. Detectron2 for Ojbect Detection/assets/Object-Detection-with-Detctron2.ipynb 13.9 MB
  • 02. What is Computer Vision & its Applications/02. Major Computer Vision Tasks.mp4 11.8 MB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/02. UNet Architecture For Segmentation.mp4 9.1 MB
  • 05. 1. Image Classification Task of Computer Vision/01. Image Classification Task of Computer Vision with Pytoch and Python.mp4 7.1 MB
  • 26. Complete Code and Custom Dataset for Instance Segmentation/assets/Instance-Segmentation-on-Custom-Dataset.zip 6.7 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO-You-Only-Look-Once.pdf 5.3 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO2.pdf 5.3 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO4.pdf 3.9 MB
  • 27. Video Instance Segmentation/assets/VehicleSegmentation.zip 3.4 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO3.pdf 2.5 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO7.pdf 2.3 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO4-CSPNet.pdf 1.5 MB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO6.pdf 1.1 MB
  • 10. Video Object Detection in Real-time/assets/VehiclesDetection.ipynb 1.1 MB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/assets/Lecture-2-Resources-Single-Label-Classification.zip 1.0 MB
  • 22. Complete Code and Dataset for Semantic Segmentation/assets/Lecture-2-Final-Code.zip 961.4 kB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO5-EfficientNet.pdf 770.3 kB
  • 09. Overview of YOLO Family for Object Detection/assets/YOLO5.pdf 751.6 kB
  • 27. Video Instance Segmentation/assets/Code-Instance-Segmentation.zip 372.1 kB
  • 05. 1. Image Classification Task of Computer Vision/assets/Image-Classification-with-Deep-CNN.zip 246.5 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/assets/Lecture-2-Resources-Multi-Label-Classification.zip 232.7 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/assets/Resources-Code-for-Convolutional-Neural-Networks-from-Scratch-with-Python.zip 183.9 kB
  • 07. Transfer Learning for Image Classification/assets/Code-for-Transfer-Learning-by-FineTuning-and-Model-Feature-Extractor.zip 123.4 kB
  • 09. Overview of YOLO Family for Object Detection/01. Overview of YOLO Family.vtt 42.2 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/03. Coding Convolutional Neural Network Architecture from Scratch.vtt 35.9 kB
  • 11. Overview of RCNN Family for Object Detection/01. Overview of RCNN Family for Object Detection.vtt 31.2 kB
  • 05. 1. Image Classification Task of Computer Vision/02. Image Classification with Deep Convolutional Neural Networks using Python.vtt 30.2 kB
  • 03. Deep Learning for Computer Vision/01. Basics of Deep Learning for Computer Vision.vtt 28.3 kB
  • 25. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset/02. Train, Evaluate Instance Segmentation Model & Visualizing Results on Custom Data.vtt 23.7 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/04. Data Loading with PyTorch Customized Dataset Class.vtt 23.5 kB
  • 12. Detectron2 for Ojbect Detection/01. Detectron2 for Ojbect Detection with PyTorch.vtt 23.2 kB
  • 20. Implementation, Optimization and Training Of Segmentation Models/01. Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, and UNet++).vtt 20.8 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/06. Calculate Accuracy, Precision, Recall and Visualize Confusion Matrix.vtt 19.3 kB
  • 18. Performance Metrics (IOU) For Segmentation Models Evaluation/02. Code (Python and PyTorch).html 18.8 kB
  • 27. Video Instance Segmentation/02. YOLO8 for Video Instance Segmentation.vtt 17.1 kB
  • 09. Overview of YOLO Family for Object Detection/03. YOLOv8 and its Architecture.vtt 17.1 kB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/03. Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset.vtt 16.8 kB
  • 21. Test Models and Visualize Segmentation Results/03. Visualize Segmentation Results and Generate RGB Segmented Map.vtt 16.7 kB
  • 25. Training, Evaluating and Visualizing Instance Segmentation on Custom Dataset/01. Custom Dataset for Instance Segmentation.vtt 16.2 kB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/01. Custom Dataset for Object Detection.vtt 16.1 kB
  • 21. Test Models and Visualize Segmentation Results/01. Test Models and Calculate IOU,Pixel Accuracy,Fscore.vtt 15.9 kB
  • 15. 3. Semantic Segmentation Task Of Computer Vision/02. Semantic Segmentation Real-World Applications.vtt 14.3 kB
  • 02. What is Computer Vision & its Applications/01. Introduction to Computer Vision and its Real-world Applications.vtt 13.6 kB
  • 19. Encoders and Decoders For Segmentation In PyTorch/02. Encoders for Segmentation with PyTorch Liberary.vtt 13.5 kB
  • 18. Performance Metrics (IOU) For Segmentation Models Evaluation/01. Performance Metrics (IOU, Pixel Accuracy, Precision, Recall, Fscore).vtt 13.4 kB
  • 19. Encoders and Decoders For Segmentation In PyTorch/03. Decoders for Segmentation in PyTorch Liberary.vtt 13.0 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/06. Data Augmentation using Albumentations with Different Transformations.vtt 13.0 kB
  • 12. Detectron2 for Ojbect Detection/02. Perform Object Detection using Detectron2 Pretrained Models.vtt 12.8 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/04. Convolutional Neural Networks HyperParameters Optimization.vtt 12.5 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/01. Computer Vision using Convolutional Neural Networks (CNN).vtt 11.9 kB
  • 20. Implementation, Optimization and Training Of Segmentation Models/05. Training of Segmentation Models.vtt 11.8 kB
  • 20. Implementation, Optimization and Training Of Segmentation Models/06. Model Training Code (Python And PyTorch).html 11.5 kB
  • 21. Test Models and Visualize Segmentation Results/02. Test Models and Calculate Performance Scores (Python Code).html 11.1 kB
  • 20. Implementation, Optimization and Training Of Segmentation Models/03. Learn To Optimize Hyperparameters For Segmentation Models.vtt 10.7 kB
  • 19. Encoders and Decoders For Segmentation In PyTorch/01. Transfer Learning And Pretrained Deep Resnet Architecture.vtt 10.6 kB
  • 08. 2. Object Detection Task Of Computer Vision/01. Object Detection Task Of Computer Vision with Python.vtt 9.4 kB
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/04. Python and PyTorch Code.html 9.4 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/05. Single-Label Image Classification using ResNet and AlexNet PreTrained Models.vtt 9.3 kB
  • 07. Transfer Learning for Image Classification/02. Dataset, Data Augmentation, and Dataloaders.vtt 9.3 kB
  • 07. Transfer Learning for Image Classification/04. FineTuning Deep ResNet Model.vtt 8.7 kB
  • 10. Video Object Detection in Real-time/05. Calculate Performance Metrics (Precision, Recall, Mean Average Precision mAP).vtt 8.7 kB
  • 10. Video Object Detection in Real-time/04. Testing YOLO8 on Videos and Images.vtt 8.5 kB
  • 10. Video Object Detection in Real-time/02. Setting HyperParameters for YOLO8.vtt 8.4 kB
  • 07. Transfer Learning for Image Classification/01. Introduction to Transfer Learning.vtt 8.1 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/07. Multi-Label Image Classification using Deep Learning Models.vtt 8.1 kB
  • 27. Video Instance Segmentation/04. Setting-up Hyper Parameters for Model.vtt 8.0 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/02. Setting-up Google Colab for Writing Python Code.vtt 8.0 kB
  • 07. Transfer Learning for Image Classification/08. Model Optimization, Training and Results Visualization.vtt 8.0 kB
  • 07. Transfer Learning for Image Classification/05. HyperParameteres Optimization for Model.vtt 7.8 kB
  • 15. 3. Semantic Segmentation Task Of Computer Vision/01. Semantic Segmentation Task Of Computer Vision with Pytorch and Python.vtt 7.4 kB
  • 10. Video Object Detection in Real-time/03. Training YOLO8 on Vehicles Dataset.vtt 7.4 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/01. Datasets for Semantic Segmentation.vtt 7.3 kB
  • 27. Video Instance Segmentation/05. Training Video Instance Segmentation Model.vtt 7.2 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/02. Data Annotations Tool for Semantic Segmentation.vtt 7.1 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/05. Data Loading for Segmentation with Python and PyTorch Code.html 6.9 kB
  • 10. Video Object Detection in Real-time/01. Vehicles Detection Custom Dataset.vtt 6.9 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/08. Learn To Implement Data Loaders In Pytorch.vtt 6.5 kB
  • 27. Video Instance Segmentation/03. Custom Dataset for Instance Segmentation.vtt 6.5 kB
  • 27. Video Instance Segmentation/07. Testing Segmentation Model on Videos.vtt 6.4 kB
  • 23. 4. Instance Segmentation Task of Computer Vision/01. Instance Segmentation Task of Computer Vision with Python.vtt 6.4 kB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/01. Pyramid Scene Parsing Network (PSPNet) For Segmentation.vtt 6.0 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/01. Introduction to Pretrained Models.vtt 5.8 kB
  • 07. Transfer Learning for Image Classification/07. Fixed Feature Extractraction using ResNet.vtt 5.8 kB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/04. Multi-Task Contextual Network (MTCNet).vtt 5.7 kB
  • 27. Video Instance Segmentation/01. Intro to Video Instance Segmentation.vtt 5.5 kB
  • 24. Mask RCNN for Instance Segmentation/01. Mask RCNN for Instance Segmentation.vtt 5.5 kB
  • 12. Detectron2 for Ojbect Detection/03. Python and PyTorch Code.html 5.3 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/02. Deep Learning ResNet and AlexNet Architectures.vtt 5.1 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/04. Data Preprocessing for Image Classification.vtt 5.1 kB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/02. UNet Architecture For Segmentation.vtt 4.9 kB
  • 27. Video Instance Segmentation/06. Testing Segmentation Model on Images.vtt 4.7 kB
  • 28. Bonus Lecture Video Object Detection and Video Segmentation with Python/01. Bonus Lecture Video Object Detection and Video Segmentation with Python.html 4.6 kB
  • 16. Deep Learning Architectures For Segmentation (UNet, PSPNet, PAN)/03. Pyramid Attention Network (PAN).vtt 4.5 kB
  • 07. Transfer Learning for Image Classification/06. Training Deep ResNet Model.vtt 4.3 kB
  • 22. Complete Code and Dataset for Semantic Segmentation/01. Final Code Review.vtt 4.1 kB
  • 01. Introduction to Course/01. Introduction to Computer Vision Course.vtt 4.0 kB
  • 02. What is Computer Vision & its Applications/02. Major Computer Vision Tasks.vtt 3.8 kB
  • 04. Computer Vision and Deep Convolutional Neural Networks/05. Training Convolutional Neural Network from Scratch.vtt 3.6 kB
  • 10. Video Object Detection in Real-time/06. Export and Deploy the Model.vtt 3.4 kB
  • 06. Pretrained Models for Single and Multi-Label Image Classification/03. Access Data from Google Drive to Colab.vtt 3.4 kB
  • 27. Video Instance Segmentation/08. Deploy Video Segmentation Model.vtt 3.4 kB
  • 05. 1. Image Classification Task of Computer Vision/01. Image Classification Task of Computer Vision with Pytoch and Python.vtt 3.3 kB
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/07. Augmentation Python Code.html 2.8 kB
  • 21. Test Models and Visualize Segmentation Results/04. Segmentation Results Visualization (Python Code).html 2.7 kB
  • 20. Implementation, Optimization and Training Of Segmentation Models/02. Segmentation Models Code with Python.html 1.0 kB
  • 20. Implementation, Optimization and Training Of Segmentation Models/04. Model Optimaztion Code (Python And PyTorch).html 292 Bytes
  • 05. 1. Image Classification Task of Computer Vision/03. Resources Code for Image Classification with Deep CNN from Scratch.html 166 Bytes
  • 04. Computer Vision and Deep Convolutional Neural Networks/07. Resources Complete Code for CNN from Scratch with Python and Pytorch.html 141 Bytes
  • 27. Video Instance Segmentation/09. Resources Video Segmentation Complete Code and Dataset.html 120 Bytes
  • 17. Segmentation Datasets, Annotations, Data Augmentation & Data Loading/03. Dataset for Semantic Segmentation.html 119 Bytes
  • 06. Pretrained Models for Single and Multi-Label Image Classification/06. Single Label Classification Python and Pytorch Code.html 114 Bytes
  • 22. Complete Code and Dataset for Semantic Segmentation/02. Complete Code and Dataset is Attached.html 106 Bytes
  • 06. Pretrained Models for Single and Multi-Label Image Classification/08. Multi-Label Classification Python and PyTorch Code.html 101 Bytes
  • 26. Complete Code and Custom Dataset for Instance Segmentation/01. Resources Complete Code and Custom Dataset for Instance Segmentation.html 90 Bytes
  • 10. Video Object Detection in Real-time/07. Resources Videos Vehicles Detection Complete Code and Dataset.html 88 Bytes
  • 07. Transfer Learning for Image Classification/09. Complete Python Code for Transfer Learning and Dataset.html 77 Bytes
  • 14. Complete Code and Custom Dataset for Object Detection/01. Resources Code and Custom Dataset for Object Detection.html 76 Bytes
  • 07. Transfer Learning for Image Classification/03. Dataset for Classification.html 69 Bytes
  • 09. Overview of YOLO Family for Object Detection/02. YOLO Family Research Papers.html 66 Bytes
  • 13. Training, Evaluating and Visualizing Object Detection on Custom Dataset/02. Dataset for Object Detection.html 56 Bytes

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