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Waifu2x-Extension-GUI

AaronFeng753 / Waifu2x-Extension-GUI

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Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, SRMD, RealSR, Anime4K, RIFE, CAIN, DAIN, and ACNet.

video anime vulkan waifu2x video-processing super-resolution image-enlarger noise-reduction upscaling ncnn frame-interpolation video-super-resolution video-interpolation video-enlarger video-frame-interpolation esrgan dain anime4k waifu2x-ncnn-vulkan realsr
  • Updated Oct 22, 2021
  • C++

Tencent / TNN

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TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.

ocr deep-learning tensorflow inference pytorch tengine face-detection tensorrt mnn coreml ncnn openvino hairsegmentaion
  • Updated Oct 25, 2021
  • C++

DefTruth / lite.ai.toolkit

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Lite.AI.ToolKit 🚀🚀🌟: A lite C++ toolkit of awesome AI models(一个开箱即用的C++ AI工具箱，支持ONNXRuntime/NCNN/MNN/TNN), such as RobustVideoMatting🔥, YOLOX🔥, YOLOP🔥 etc. (https://github.com/DefTruth/lite.ai.toolkit/releases/tag/v0.1.0)

segmentation face-recognition face-detection object-detection matting face-landmarks mnn ncnn arcface insightface cosface retinaface onnxruntime tnn nanodet scaled-yolov4 yolor yolox yolop robustvideomatting
  • Updated Oct 26, 2021
  • C++
yolov5-rt-stack

zhiqwang / yolov5-rt-stack

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Add a dataloader as ultralytics in detection pipeline

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zhiqwang
zhiqwang commented Apr 19, 2021

The outputs are great, although not the same as yolov5, maybe some pre-processing/post-processing steps are different.

That's a great catch! I think it is caused by the different pre-processing operations. I've verified the the post-processing stages before, it can get the same results as ultralytics/yolov5 (when w/o TTA predict). And I've uploaded a [notebook](https://github.com/zhiqwang/yol

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bug / fix enhancement good first issue

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