ShuffleNetV2 (Garcia Ling et al., 2020) by @seed detects visual glitches in video games using neural networks (~87% accuracy, ~9% false positive). The model is able to detect glitches in objects unseen during training. Blog: https://t.co/CPxLybKnTa Paper: https://t.co/on2APg20af

試しに、入力解像度を併せて比較してみた。上：公式のShuffleNetV2 120MB、下：YOLOv7-tiny_Head 20MB。YOLOv7-tinyのほうが圧倒的に強かった。そして、向き判定ミスってる。やっぱり、自分でエッジ用途に鬼カスタマイズしたほうが精度も速度も上げられそう。

https://t.co/4LdcqM7gSY New post at DebuggerCafe - Transfer Learning using PyTorch ShuffleNetV2 #PyTorch #ImageClassification #ShuffleNet #DeepLearning #ComputerVision

@wightmanr @giffmana @ChrSzegedy @ykilcher @CameronTurner55 ShuffleNetv2 ( https://t.co/ewZ2bTRXWO) also compares its weights with DenseNet, as they approximate DenseNet's connections efficiently using channel-shuffle. Worth a read, as they even outperformed MobileNet.

Intel の person-detection-asl-0001 を量子化してコミットしました。カオスです。"It is based on ShuffleNetV2-like backbone that includes depth-wise convolutions to reduce the amount of computation for the 3x3 convolution block and FCOS head." https://t.co/nYYiB0dPJP

Highly Accessed Articles of @Agronomy_Mdpi ✨ Edge Device Detection of Tea Leaves with One Bud and Two Leaves Based on ShuffleNetv2-YOLOv5-Lite-E 🔗 Accessible at: https://t.co/J1mlJzawSD 👉See more about：

Main takeaways: -ShuffleNetV2 did a nice job with energy efficiency but that has been ignored by our industry -accuracy has not improved as much as energy consumption has increased -batching inference tasks reduces energy consumption -…more in the paper: https://t.co/sNLOjGdZEn

This paper benchmarks five lightweight architectures: MobileNetV3 Small, ResNet18, SqueezeNet, EfficientNetV2-S, and ShuffleNetV2. It tests them across CIFAR-10, CIFAR-100, and Tiny ImageNet. The paper analyzes accuracy, inference time, Floating-Point Operations (FLOPs), and

Augshufflenet, A more efficient model compared to Shufflenetv2, https://t.co/EdNYKFaoY3

ShuffleNetV2+なるものがあった。h-swishとse moduleが追加されている。完全にMobileNetV3, MnasNet, EfficientNetと同じ方向性なので、この2つはデファクト感漂ってきた / “ShuffleNet-Series/ShuffleNetV2+ at master · megvii-model/ShuffleNet-Series · GitHub”

Working on an Android app to help visually impaired folks navigate better using image segmentation. Excited about the impact! #accessibility #technology #assistive #tech #android #java #AI #Mobility #python #shufflenetv2 #mobilenetv2 #TensorFlow

Monday in @SCeCONF I will present one of @iWERS_UofSC latest studies on water textures and inherent color properties. We applied and compared the performance of #Gabor_wavelets, #Local_Binary_Patterns, and #ShuffleNetV2 on #ATeX water images in classification of waterbodies.

気になったこと shufflenetv2の主張 ・1×1convは入出力チャネル数を同じにしないとメモリアクセスが増えて？遅くなる（=mobilenetV2の完全否定） ・groupedconvはメモリアクセスが遅い（知ってた、group2でも割と遅い） ・モジュールを細分化しすぎると並列度低下 ・要素毎の演算も避けるべし

🔥 Read our Paper 📚 Artificial Intelligence-Driven Eye Disease Classification Model 🔗 https://t.co/wof7vFWXKF 👨‍🔬 by Abdul Rahaman Wahab Sait. 🏫 King Faisal University #artificialintelligence #ShuffleNetV2 #oculardiseases #machinelearning

We developed an improved DeepLabv3+ algorithm using ShuffleNetV2 to accurately detect and localize safflower filaments. It achieved 95.84% pixel accuracy and 96.87% intersection over union. #Harvestingrobots Details: https://t.co/kvRPkWCKQI

Intelligent Fault Diagnosis Method Based on VMD-Hilbert Spectrum and ShuffleNet-V2: Application to the Gears in a Mine Scraper Conveyor Gearbox https://t.co/a2iTfYmdgy #MCSA #loadimpact #VMD #GA #Hilbertspectrum #ShuffleNetV2

#AgricultureMdpi - 2022 High Cited Paper Title: A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX Authors: Wei Ji et al. Link: https://t.co/GSUfhMQSlN #machinevision #pickingrobot #appledetection #YOLOX #ShufflenetV2

Harvesting robots struggle with small, numerous filaments and near-colored backgrounds. We propose an improved DeepLabv3+ method with ShuffleNetV2 and convolutional branches for accurate filament picking. Details: https://t.co/AQw3Grdd8G

#AgricultureMdpi – Editor's Choice Article A Real-Time Apple Targets Detection Method for Picking Robot Based on ShufflenetV2-YOLOX by Wei Ji et al. Link: https://t.co/GSUfhMQSlN #machinevision #pickingrobot #appledetection #YOLOX #ShufflenetV2

New method for detecting & localizing safflower filaments using improved DeepLabv3+ & ShuffleNetV2. Achieves 95.84% pixel accuracy, 96.87% IoU, and 92.50% localization success rate. A game-changer for robotic harvesting! Details: https://t.co/AQw3GrdKYe