Day-62 Computer Vision Learning DCN / DCNv1 — Deformable Convolutional Networks, 2nd Runner Up in 2017 COCO Detection (Object Detection) by Microsoft Research Asia Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 It is published in 2017 ICCV, which has already got over 1551 citations. 🔸 With Deformable Convolution, Improved Faster R-CNN and R-FCN, Got 2nd Runner Up in COCO Detection & 3rd Runner Up in COCO Segmentation. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/e4K4Tna Official Code : https://bit.ly/2OdND6i tensorflow : https://bit.ly/3uKRSap pytorch : https://bit.ly/3bYDMcQ keras : https://bit.ly/3e5ZGh4 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Traditional/regular convolution is based on a defined filter size and operates on a predefined rectangular grid of an input image or a set of input feature maps . The size of the grid can be 3×3, 5×5, and so on. However, the objects we want to detect and classify may be deformed or occluded in the image. #computervision #artificialintelligence #technology
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🔸 In DCN , the grid is deformable because each grid point can be moved by a learnable offset . Convolution acts on these moving grid points , so it is called deformable convolution For previous post visit this github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post
Hello, can u help in categorical columns and nemerical columns for predictions (multiclassification problem)