#cplusplus #anime4k #c_plus_plus #cpp #gif_enlarger #gpu #image_enlarger #machine_learning #ncnn #neural_network #noise_reduction #qt #srmd #super_resolution #video #video_enlarger #vulkan #waifu2x
https://github.com/AaronFeng753/Waifu2x-Extension-GUI
https://github.com/AaronFeng753/Waifu2x-Extension-GUI
GitHub
GitHub - AaronFeng753/Waifu2x-Extension-GUI: Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation.…
Video, Image and GIF upscale/enlarge(Super-Resolution) and Video frame interpolation. Achieved with Waifu2x, Real-ESRGAN, Real-CUGAN, RTX Video Super Resolution VSR, SRMD, RealSR, Anime4K, RIFE, I...
#python #anchor_free #deep_learning #deep_neural_networks #ncnn #object_detection #pytorch #shufflenet
https://github.com/RangiLyu/nanodet
https://github.com/RangiLyu/nanodet
GitHub
GitHub - RangiLyu/nanodet: NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB…
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥 - RangiLyu/nanodet
#python #tensorrt #ncnn #onnx #yolov3 #openvino #yolox #yolox_nano
https://github.com/Megvii-BaseDetection/YOLOX
https://github.com/Megvii-BaseDetection/YOLOX
GitHub
GitHub - Megvii-BaseDetection/YOLOX: YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT…
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/ - Megvii-BaseDetection/Y...
#cplusplus #deployment #model_converter #ncnn #onnxruntime #openvino #pplnn #sdk #tensorrt
https://github.com/open-mmlab/mmdeploy
https://github.com/open-mmlab/mmdeploy
GitHub
GitHub - open-mmlab/mmdeploy: OpenMMLab Model Deployment Framework
OpenMMLab Model Deployment Framework. Contribute to open-mmlab/mmdeploy development by creating an account on GitHub.
#c_lang #amd #gpu #intel #linux #macos #ncnn #nvidia #realcugan #vulkan #windows
https://github.com/nihui/realcugan-ncnn-vulkan
https://github.com/nihui/realcugan-ncnn-vulkan
GitHub
GitHub - nihui/realcugan-ncnn-vulkan: real-cugan converter ncnn version, runs fast on intel / amd / nvidia / apple-silicon GPU…
real-cugan converter ncnn version, runs fast on intel / amd / nvidia / apple-silicon GPU with vulkan - nihui/realcugan-ncnn-vulkan
#python #anime4k #machine_learning #ncnn #neural_network #qt5 #realsr #rife #srmd #super_resolution #upscaling #video #video_enlarger #vulkan #waifu2x
https://github.com/k4yt3x/video2x
https://github.com/k4yt3x/video2x
GitHub
GitHub - k4yt3x/video2x: A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley…
A machine learning-based video super resolution and frame interpolation framework. Est. Hack the Valley II, 2018. - k4yt3x/video2x
#cplusplus #caffe #convolution #deep_learning #deep_neural_networks #diy #graph_algorithms #inference #inference_engine #maxpooling #ncnn #pnnx #pytorch #relu #resnet #sigmoid #yolo #yolov5
This course, "_动手自制大模型推理框架_" (Handcrafting Large Model Inference Framework), is a valuable resource for those interested in deep learning and model inference. It teaches you how to build a modern C++ project from scratch, focusing on designing and implementing a deep learning inference framework. The course supports latest models like LLama3.2 and Qwen2.5, and uses CUDA acceleration and Int8 quantization for better performance.
By taking this course, you will learn how to write efficient C++ code, manage projects with CMake and Git, design computational graphs, implement common operators like convolution and pooling, and optimize them for speed. This knowledge will be highly beneficial for job interviews and advancing your skills in deep learning. The course also includes practical demos on models like Unet and YoloV5, making it a hands-on learning experience.
https://github.com/zjhellofss/KuiperInfer
This course, "_动手自制大模型推理框架_" (Handcrafting Large Model Inference Framework), is a valuable resource for those interested in deep learning and model inference. It teaches you how to build a modern C++ project from scratch, focusing on designing and implementing a deep learning inference framework. The course supports latest models like LLama3.2 and Qwen2.5, and uses CUDA acceleration and Int8 quantization for better performance.
By taking this course, you will learn how to write efficient C++ code, manage projects with CMake and Git, design computational graphs, implement common operators like convolution and pooling, and optimize them for speed. This knowledge will be highly beneficial for job interviews and advancing your skills in deep learning. The course also includes practical demos on models like Unet and YoloV5, making it a hands-on learning experience.
https://github.com/zjhellofss/KuiperInfer
GitHub
GitHub - zjhellofss/KuiperInfer: 校招、秋招、春招、实习好项目!带你从零实现一个高性能的深度学习推理库,支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance…
校招、秋招、春招、实习好项目!带你从零实现一个高性能的深度学习推理库,支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step - zjhellofss/KuiperInfer