Day-12 Computer Vision Learning RetinaNet : Focal Loss for Dense Object Detection 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗳𝗮𝗰𝘁𝘀 🔸 RetineNet is a 2017 ICCV Best Student Paper Award paper with more than 500 citations. 🔸 The first author, Tsung-Yi Lin, has become Research Scientist at Google Brain when he was presenting RetinaNet in 2017 ICCV 🔸 Focal Loss First time introduced in this paper. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://bit.ly/35xCHGk 𝗣𝘆𝘁𝗼𝗿𝗰𝗵 RetinaNet : https://bit.ly/2KaRQWO 𝘁𝗳-𝗸𝗲𝗿𝗮𝘀 RetinaNet : https://bit.ly/35vqm5q ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 RetinaNet is a one-stage object detection model that utilizes a focal loss function to address class imbalance during training. Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard negative examples. 🔸 RetinaNet is a single, unified network composed of a backbone network and two task-specific subnetworks. 🔸 The backbone is responsible for computing a convolutional feature map over an entire input image and is an off-the-self convolutional network. Note: More in comments #innovation #artificialintelligence #computervision
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Focal Loss (FL) is an improved version of Cross-Entropy Loss (CE) that tries to handle the class imbalance problem by assigning more weights to hard or easily misclassified examples
Two-Stage Detectors Architecture of #Retinanet 🔸 In two-stage detectors such as Faster R-CNN, the first stage, region proposal network (RPN) narrows down the number of candidate object locations to a small number (e.g. 1–2k), filtering out most background samples. 🔸 At the second stage, classification is performed for each candidate object location. Sampling heuristics using fixed foreground-to-background ratio (1:3), or online hard example mining (OHEM) to select a small set of anchors (e.g., 256) for each minibatch. #technology #deeplearning #india
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