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Object Detection Part 1: R-CNN, Sliding Window and Selective Search

This is the first video in the object detection series and in it we are exploring the definition of object detection in computer vision, how we can approach this task using the sliding window algorithm and how the Region-based Convolutional Neural Network (R-CNN) model improves this approach by employing the selective search region proposal algorithm. References ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ "Rich feature hierarchies for accurate object detection and semantic segmentation" paper: https://arxiv.org/abs/1311.2524 "Selective Search for Object Recognition" paper: http://www.huppelen.nl/publications/s... Selective search algorithm (more details): https://learnopencv.com/selective-sea... Related Videos ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Object Detection Part 2: Fast R-CNN, Region Projection and Region of Interest (RoI) Pooling Layer:    • Object Detection Part 2: Fast R-CNN, Regio...   Object Detection Part 3: Faster R-CNN, Region Proposal Network and Intersection over Union:    • Object Detection Part 3: Faster R-CNN, Reg...   Why Neural Networks Can Learn Any Function:    • Why Neural Networks Can Learn Any Function   Why Deep Neural Networks (DNNs) Underperform Tree-Based Models on Tabular Data:    • Why Deep Neural Networks (DNNs) Underperfo...   Why Residual Connections (ResNet) Work:    • Why Residual Connections (ResNet) Work   Contents ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 00:00 - Intro 00:14 - Object Detection Definition 00:59 - Sliding Window 02:00 - R-CNN Model 02:32 - Selective Search Algorithm 04:27 - Cons of Using R-CNN 05:20 - Outro Follow Me ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic   / datamlistic   📸 Instagram: @datamlistic   / datamlistic   📱 TikTok: @datamlistic   / datamlistic   Channel Support ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon:   / datamlistic   ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a #cnn #rcnn #objectdetection #selectivesearch