Day-68 Computer Vision Learning ERFNet — Efficient Residual Factorized ConvNet for Real-time (Semantic Segmentation) Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 It is published in 2017 TITS , which has already got over 436 citations. 🔸 Outperforms DilatedNet, DPN, FCN, DeepLabv1, ENet & SegNet, Similar accuracy to SOTA, RefineNet & DeepLabv2, While Taking Only 24ms Per Image on a Single GPU ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://bit.ly/3btlnWo Official Code(Pytorch) : https://bit.ly/3buGY0D ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 A novel layer that uses residual connections and factorized convolutions, is proposed in order to remain efficient while retaining remarkable accuracy. 🔸 ERFNet is able to run at over 83 FPS in a single Titan X, and 7 FPS in a Jetson TX1 (embedded GPU). #computervision #artificialintelligence #innovation
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5yFor previous post visit this github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post