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MSG-NetDepth Map Super-Resolution by Deep Multi-Scale Guidance, ECCV 2016
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SESRSESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
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srganPytorch implementation of "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network"
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autodialAutoDIAL Caffe Implementation
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fast-image-retrievalA lightweight framework using binary hash codes and deep learning for fast image retrieval.
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ddrlDeep Developmental Reinforcement Learning
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esrganEnhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution
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TMNetThe official pytorch implemention of the CVPR paper "Temporal Modulation Network for Controllable Space-Time Video Super-Resolution".
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XLearning-GPUqihoo360 xlearning with GPU support; AI on Hadoop
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Video2xA lossless video/GIF/image upscaler achieved with waifu2x, Anime4K, SRMD and RealSR. Started in Hack the Valley 2, 2018.
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TensorRT-LPR车牌识别,基于HyperLPR实现,修改模型调用方法,使用caffe+tensorRT实现GPU加速,修改了车牌检测模型
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uai-sdkUCloud AI SDK
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PolyaxonMachine Learning Platform for Kubernetes (MLOps tools for experimentation and automation)
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Real-ESRGAN-colabA Real-ESRGAN model trained on a custom dataset
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traiNNertraiNNer: Deep learning framework for image and video super-resolution, restoration and image-to-image translation, for training and testing.
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MIRNet-KerasKeras Implementation of MIRNet - SoTA in Image Denoising, Super Resolution and Image Enhancement - CVPR 2020
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R2CNNcaffe re-implementation of R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection
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FacedetectionC++ project to implement MTCNN, a perfect face detect algorithm, on different DL frameworks. The most popular frameworks: caffe/mxnet/tensorflow, are all suppported now
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CAM-PythonClass Activation Mapping with Caffe using the Python wrapper pycaffe instead of matlab.
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PAM[TPAMI 2020] Parallax Attention for Unsupervised Stereo Correspondence Learning
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SwinIRSwinIR: Image Restoration Using Swin Transformer (official repository)
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facial-landmarksFacial landmarks detection with OpenCV, Dlib, DNN
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caffe-demoCollection of deep learning demos based on neworks from the Caffe Zoo
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Caffe HrtHeterogeneous Run Time version of Caffe. Added heterogeneous capabilities to the Caffe, uses heterogeneous computing infrastructure framework to speed up Deep Learning on Arm-based heterogeneous embedded platform. It also retains all the features of the original Caffe architecture which users deploy their applications seamlessly.
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SinganOfficial pytorch implementation of the paper: "SinGAN: Learning a Generative Model from a Single Natural Image"
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