Канал: Cogneethi

Stylegan2 Experiments - Projecting real images of Johnny Depp

Stylegan2 Experiments - Projecting real images of Johnny Depp

Light a Candle | Evolution of Object Detection Networks | Cogneethi

Light a Candle | Evolution of Object Detection Networks | Cogneethi

My Rant | Chain Of Influences | CNN | Object Detection | Machine learning | EvODN

My Rant | Chain Of Influences | CNN | Object Detection | Machine learning | EvODN

Request

Request

Chapter 7 Guide | CNN | Object Detection | EvODN

Chapter 7 Guide | CNN | Object Detection | EvODN

Chapter 8 Guide | CNN | Object Detection | EvODN

Chapter 8 Guide | CNN | Object Detection | EvODN

Chapter 5 Guide | CNN | Object Detection | EvODN

Chapter 5 Guide | CNN | Object Detection | EvODN

General Comment on the Style of this Tutorial | CNN | Object Detection | EvODN

General Comment on the Style of this Tutorial | CNN | Object Detection | EvODN

Chapter 1-4 Guide | CNN | Object Detection | EvODN

Chapter 1-4 Guide | CNN | Object Detection | EvODN

C 8.5 | Faster RCNN | Region Proposal Network (RPN) And Overfeat | CNN | Machine Learning | EvODN

C 8.5 | Faster RCNN | Region Proposal Network (RPN) And Overfeat | CNN | Machine Learning | EvODN

C 8.9 | Faster RCNN Network - Code Walkthrough | CNN | Object Detection | Machine learning | EvODN

C 8.9 | Faster RCNN Network - Code Walkthrough | CNN | Object Detection | Machine learning | EvODN

C 8.8 | Faster RCNN Demo - BBox Refinement, Confidence Score, Non Max Suppression | Machine Learning

C 8.8 | Faster RCNN Demo - BBox Refinement, Confidence Score, Non Max Suppression | Machine Learning

C 8.7 | Faster RCNN Demo - Pixel Norm, Conv Feature Maps and RPN BBoxes | CNN | Machine Learning

C 8.7 | Faster RCNN Demo - Pixel Norm, Conv Feature Maps and RPN BBoxes | CNN | Machine Learning

C 8.6 | Quirks About Anchor Boxes | CNN | Object Detection | Machine learning | EvODN

C 8.6 | Quirks About Anchor Boxes | CNN | Object Detection | Machine learning | EvODN

C 8.4 | Training Faster RCNN Network | CNN | Object Detection | Machine learning | EvODN

C 8.4 | Training Faster RCNN Network | CNN | Object Detection | Machine learning | EvODN

C 8.3 | Faster RCNN - Optimizing Region Proposal Network | CNN | Object Detection | Machine learning

C 8.3 | Faster RCNN - Optimizing Region Proposal Network | CNN | Object Detection | Machine learning

C 8.2 | Faster RCNN - Reducing Proposals from Region Proposal Network | Machine Learning | EvODN

C 8.2 | Faster RCNN - Reducing Proposals from Region Proposal Network | Machine Learning | EvODN

C 8.1 | Faster RCNN | Absolute vs Relative BBOX Regression | Anchor Boxes | CNN | Machine Learning

C 8.1 | Faster RCNN | Absolute vs Relative BBOX Regression | Anchor Boxes | CNN | Machine Learning

C 8.0 | Faster RCNN - Problem Statement | CNN | Object Detection | Machine learning | EvODN

C 8.0 | Faster RCNN - Problem Statement | CNN | Object Detection | Machine learning | EvODN

C 7.9 | Fast RCNN Network, Computation Time, Accuracy | CNN | Object Detection | Machine learning

C 7.9 | Fast RCNN Network, Computation Time, Accuracy | CNN | Object Detection | Machine learning

C 7.8 | SPPNet - Computation Time & Accuracy | Fast RCNN | CNN | Object Detection | Machine learning

C 7.8 | SPPNet - Computation Time & Accuracy | Fast RCNN | CNN | Object Detection | Machine learning

C 7.7 | BBox Regressor Training | SPPNet | Fast RCNN | CNN | Machine Learning | Object Detection

C 7.7 | BBox Regressor Training | SPPNet | Fast RCNN | CNN | Machine Learning | Object Detection

C 7.6 | ROI Pooling | SPPNet | Fast RCNN | CNN | Machine Learning | Object Detection | EvODN

C 7.6 | ROI Pooling | SPPNet | Fast RCNN | CNN | Machine Learning | Object Detection | EvODN

C 7.1 | Bag Of Visual Words | CNN | Object Detection | Machine learning | EvODN

C 7.1 | Bag Of Visual Words | CNN | Object Detection | Machine learning | EvODN

C 7.5 | ROI Projection | Subsampling ratio | SPPNet | Fast RCNN | CNN | Machine learning | EvODN

C 7.5 | ROI Projection | Subsampling ratio | SPPNet | Fast RCNN | CNN | Machine learning | EvODN

C 7.4 | SPPNet Object Detection Overview | Fast RCNN | CNN | Machine Learning | EvODN

C 7.4 | SPPNet Object Detection Overview | Fast RCNN | CNN | Machine Learning | EvODN

C 7.3 | Spatial Pyramid Pooling | SPPNet Classification | Fast RCNN | Machine learning | EvODN

C 7.3 | Spatial Pyramid Pooling | SPPNet Classification | Fast RCNN | Machine learning | EvODN

C 7.2 | Spatial Pyramid Matching | SPM | CNN | Object Detection | Machine learning | EvODN

C 7.2 | Spatial Pyramid Matching | SPM | CNN | Object Detection | Machine learning | EvODN

C 7.0 | Bag of Visual Words - Histograms | CNN | Object Detection | Machine learning | EvODN

C 7.0 | Bag of Visual Words - Histograms | CNN | Object Detection | Machine learning | EvODN

C 6.1 | RCNN - Ideas for Region Proposals | CNN | Machine Learning | Object Detection | EvODN

C 6.1 | RCNN - Ideas for Region Proposals | CNN | Machine Learning | Object Detection | EvODN

C 6.5 | RCNN General Discussion - 2 Stage Network - 3 Stage Training - Results and some Questions

C 6.5 | RCNN General Discussion - 2 Stage Network - 3 Stage Training - Results and some Questions

C 6.4 | RCNN Experiments - Do you need Fully Connected layers? Machine Learning | Object Detection

C 6.4 | RCNN Experiments - Do you need Fully Connected layers? Machine Learning | Object Detection

C 6.3 | RCNN Network Architecture | CNN | Machine Learning | Object Detection | EvODN

C 6.3 | RCNN Network Architecture | CNN | Machine Learning | Object Detection | EvODN

C 6.2 | RCNN Region Proposals - Edge Boxes & Selective Search | CNN | Machine Learning | EvODN

C 6.2 | RCNN Region Proposals - Edge Boxes & Selective Search | CNN | Machine Learning | EvODN

C 6.0 | RCNN - Problem Statement | CNN | Machine Learning | Object Detection | EvODN

C 6.0 | RCNN - Problem Statement | CNN | Machine Learning | Object Detection | EvODN

C 5.9 | Calculating Model Size of a ConvNet | 1x1 Convolution | Object Detection | Machine Learning

C 5.9 | Calculating Model Size of a ConvNet | 1x1 Convolution | Object Detection | Machine Learning

C 5.8 | Non Max Suppression | NMS | CNN | Object Detection | Machine learning | EvODN

C 5.8 | Non Max Suppression | NMS | CNN | Object Detection | Machine learning | EvODN

C 5.7 | Overfeat Non Max Suppression | NMS | CNN | Object Detection | Machine learning | EvODN

C 5.7 | Overfeat Non Max Suppression | NMS | CNN | Object Detection | Machine learning | EvODN

C 5.6 | Overfeat Network Design | Important-Dont skip | CNN | Object Detection | EvODN

C 5.6 | Overfeat Network Design | Important-Dont skip | CNN | Object Detection | EvODN

C 5.5 | Overfeat Effective Stride | Important-Dont skip | CNN | Object Detection | EvODN

C 5.5 | Overfeat Effective Stride | Important-Dont skip | CNN | Object Detection | EvODN

C 5.4 | Overfeat Intuition | Important-Dont skip | CNN | Object Detection | Machine learning | EvODN

C 5.4 | Overfeat Intuition | Important-Dont skip | CNN | Object Detection | Machine learning | EvODN

C 5.3.1 | Spatial Output for Image Pyramids | Receptive Field | CNN | Object Detection | EvODN

C 5.3.1 | Spatial Output for Image Pyramids | Receptive Field | CNN | Object Detection | EvODN

C 5.3 | ConvNet's Sliding Window Efficiency | Receptive Field | CNN | Object Detection | EvODN

C 5.3 | ConvNet's Sliding Window Efficiency | Receptive Field | CNN | Object Detection | EvODN

C 5.2 | ConvNet Input Size Constraints | CNN | Object Detection | Machine learning | EvODN

C 5.2 | ConvNet Input Size Constraints | CNN | Object Detection | Machine learning | EvODN

C 5.1 | Ideas for Object Detection | CNN | Machine Learning | EvODN

C 5.1 | Ideas for Object Detection | CNN | Machine Learning | EvODN

C 5.0 | Object Localization | Bounding Box Regression | CNN | Machine Learning | EvODN

C 5.0 | Object Localization | Bounding Box Regression | CNN | Machine Learning | EvODN

C 4.15 | Transfer Learning | CNN | Object Detection | Machine learning | EvODN

C 4.15 | Transfer Learning | CNN | Object Detection | Machine learning | EvODN

C 4.14 | Visualizing ConvNets | CNN | Object Detection | Machine Learning | EvODN

C 4.14 | Visualizing ConvNets | CNN | Object Detection | Machine Learning | EvODN

C 4.13 | Dataset - Train Test Split | CNN | Machine Learning | Object Detection | EvODN

C 4.13 | Dataset - Train Test Split | CNN | Machine Learning | Object Detection | EvODN

C 4.12 | Backpropagation Intuition | How the weights are learnt | Machine learning | EvODN

C 4.12 | Backpropagation Intuition | How the weights are learnt | Machine learning | EvODN

C 4.11 | Fully Connected Layer as Conv Layer | CNN | Object Detection | Mahine Learning | EvODN

C 4.11 | Fully Connected Layer as Conv Layer | CNN | Object Detection | Mahine Learning | EvODN

C 4.10 | Programmatically implementing Convolution | CNN | Object Detection | Machine Learning

C 4.10 | Programmatically implementing Convolution | CNN | Object Detection | Machine Learning

C 4.9 | End to End CNN Example | Convolutional Neural Network Example | Object Detection | EvODN

C 4.9 | End to End CNN Example | Convolutional Neural Network Example | Object Detection | EvODN

C 4.8 | Pooling Layer Intuition | CNN | Convolutional Neural Network | Object Detection | EvODN

C 4.8 | Pooling Layer Intuition | CNN | Convolutional Neural Network | Object Detection | EvODN

C 4.7 | Complete ConvNet | CNN | Machine Learning | Object Detection | EvODN

C 4.7 | Complete ConvNet | CNN | Machine Learning | Object Detection | EvODN

C 4.6 | Softmax | CNN | Object Detection | Machine learning | EvODN

C 4.6 | Softmax | CNN | Object Detection | Machine learning | EvODN

C 4.5 | Fully Connected Layer example | CNN | Object Detection | Machine Learning | EvODN

C 4.5 | Fully Connected Layer example | CNN | Object Detection | Machine Learning | EvODN

C 4.4 | Intuition behind Filters in CNNs | Object Detection | Machine learning | EvODN

C 4.4 | Intuition behind Filters in CNNs | Object Detection | Machine learning | EvODN

C 4.3 | Convolutional Neural Network Architecture | CNN Architecture | Object Detection | EvODN

C 4.3 | Convolutional Neural Network Architecture | CNN Architecture | Object Detection | EvODN

C 4.2 | 2D Convolution | CNN | Object Detection | Machine Learning | EvODN

C 4.2 | 2D Convolution | CNN | Object Detection | Machine Learning | EvODN

C 4.1 | 1D Convolution | CNN | Object Detection | Machine Learning | EvODN

C 4.1 | 1D Convolution | CNN | Object Detection | Machine Learning | EvODN

C 4.0 | Classical Computer Vision vs Convolutional Neural Networks | Classical CV vs CNNs | EvODN

C 4.0 | Classical Computer Vision vs Convolutional Neural Networks | Classical CV vs CNNs | EvODN

C3.13 | SIFT vs HOG | Object Detection | Machine learning | Computer Vision | EvODN

C3.13 | SIFT vs HOG | Object Detection | Machine learning | Computer Vision | EvODN

C3.11 | DPM for Rigid bodies | Object Detection | Computer Vision | Machine Learning | EvODN

C3.11 | DPM for Rigid bodies | Object Detection | Computer Vision | Machine Learning | EvODN

C3.12 | DPM Results | | Object Detection | Computer Vision | Machine learning | EvODN

C3.12 | DPM Results | | Object Detection | Computer Vision | Machine learning | EvODN

C3.10 | DPM | Deformable Parts Model | Object Detection | Machine Learning | Computer Vision | EvODN

C3.10 | DPM | Deformable Parts Model | Object Detection | Machine Learning | Computer Vision | EvODN

C39 | Dalal & Triggs Results | HOG + SVM Results | Object Detection | Computer Vision | EvODN

C39 | Dalal & Triggs Results | HOG + SVM Results | Object Detection | Computer Vision | EvODN

C37 | Dalal & Triggs Object Detection | HOG + SVM | Computer Vision | Machine Learning | EvODN

C37 | Dalal & Triggs Object Detection | HOG + SVM | Computer Vision | Machine Learning | EvODN

C34 | HOG Feature Vector Calculation | Computer Vision | Object Detection | EvODN

C34 | HOG Feature Vector Calculation | Computer Vision | Object Detection | EvODN

C36 | SVM example | Support Vector Machine | Computer Vision | Machine Learning | EvODN

C36 | SVM example | Support Vector Machine | Computer Vision | Machine Learning | EvODN

C35 | SVM Intuition | Support Vector Machine | Computer Vision | Machine Learning | EvODN

C35 | SVM Intuition | Support Vector Machine | Computer Vision | Machine Learning | EvODN

C32 | SIFT | Scale Invariant Feature Transform | Computer Vision | Object detection | EvODN

C32 | SIFT | Scale Invariant Feature Transform | Computer Vision | Object detection | EvODN

C38 | NMS | Non Max Suppression | Object Detection | Machine Learning | Computer Vision | EvODN

C38 | NMS | Non Max Suppression | Object Detection | Machine Learning | Computer Vision | EvODN

C33 | Hog Intuition | Histogram of Oriented Gradients | Computer Vision | Object Detection | EvODN

C33 | Hog Intuition | Histogram of Oriented Gradients | Computer Vision | Object Detection | EvODN

C31 | Interest Points Intuition | Computer Vision | Object Detection | EvODN

C31 | Interest Points Intuition | Computer Vision | Object Detection | EvODN

C30 | Basic problem of Comparing images | Computer Vision | Object Detection | EvODN

C30 | Basic problem of Comparing images | Computer Vision | Object Detection | EvODN

C25 | Mean Average Precision | Object Detection | Machine learning | EvODN

C25 | Mean Average Precision | Object Detection | Machine learning | EvODN

C24 | Calculating Average Precision from Precision-Recall curves | Object Detection | EvODN

C24 | Calculating Average Precision from Precision-Recall curves | Object Detection | EvODN

C23 | Precision Recall Curves for Object Detection | Machine Learning | EvODN

C23 | Precision Recall Curves for Object Detection | Machine Learning | EvODN

C22 | Calculating Precision & Recall for Object Detection | Machine Learning | EvODN

C22 | Calculating Precision & Recall for Object Detection | Machine Learning | EvODN

C21 | IoU | Intersection over Union | Object Detection | EvODN

C21 | IoU | Intersection over Union | Object Detection | EvODN

C20 | Competitions for Object Detection | ImageNet, COCO, Pascal | Machine learning | EvODN

C20 | Competitions for Object Detection | ImageNet, COCO, Pascal | Machine learning | EvODN

C15 | Receiver Operating Characteristic Curve - ROC Curve | Machine Learning

C15 | Receiver Operating Characteristic Curve - ROC Curve | Machine Learning

C14 | F1 Score vs Accuracy | Precision Recall Curve | Sensitivity vs Specificity | Machine Learning

C14 | F1 Score vs Accuracy | Precision Recall Curve | Sensitivity vs Specificity | Machine Learning

C13 | Sensitivity, Specificity, False Positive Rate | Object Detection | Machine learning | EvODN

C13 | Sensitivity, Specificity, False Positive Rate | Object Detection | Machine learning | EvODN

C12 | Terminologies - True/False Positive/Negative | Object Detection | Machine learning | EvODN

C12 | Terminologies - True/False Positive/Negative | Object Detection | Machine learning | EvODN

C11 | Evaluating ML Models - Precision/Recall Concepts | Object Detection | Machine learning

C11 | Evaluating ML Models - Precision/Recall Concepts | Object Detection | Machine learning

C10 | Evaluating ML Models - Precision/Recall Calculations | Object Detection | Machine learning

C10 | Evaluating ML Models - Precision/Recall Calculations | Object Detection | Machine learning

C01 | Whats Discussed | Object Detection | Machine learning | EvODN

C01 | Whats Discussed | Object Detection | Machine learning | EvODN

C00 | Intro to Machine Learning | Object Detection | Machine learning | EvODN

C00 | Intro to Machine Learning | Object Detection | Machine learning | EvODN