Object Detection for Dummies Part 3: R-CNN Family | Lil'Log

[Updated on 2018-12-20: Remove YOLO here. Part 4 will cover multiple fast object detection algorithms, including YOLO.] [Updated on 2018-12-27: Add bbox regression and tricks sections for R-CNN.] In the series of “Object Detection for Dummies”, we started with basic concepts in image processing, such as gradient vectors and HOG, in Part 1. Then we introduced classic convolutional neural network architecture designs for classification and pioneer models for object recognition, Overfeat and DPM, in Part 2.

Table of Contents R-CNN Model Workflow Bounding Box Regression Common Tricks Speed Bottleneck Fast R-CNN RoI Pooling Model Workflow Loss Function Speed Bottleneck Faster R-CNN Model Workflow Loss Function Mask R-CNN RoIAlign Loss Function Summary of Models in the R-CNN family Reference [Updated on 2018-12-20: Remove YOLO here. Part 4 will cover multiple fast object detection algorithms, including YOLO.] [Updated on 2018-12-27: Add bbox regression and tricks sections for R-CNN.] In the series of "Object Detection for Dummies", we started with basic concepts in image processing, such as gradient

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