RESTful Web Service and C++ compilable version of YOLO written in C and CUDA for object detection.
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Updated
Apr 12, 2017 - C
RESTful Web Service and C++ compilable version of YOLO written in C and CUDA for object detection.
Face recognition using facenet
Detect dog bark from spectrogram classification using sndfile-tools and darknet
CPU Optimized & IoT Capable Embedded Computer Vision & Machine Learning Library.
Windows and Linux version of Darknet Yolo v3 & v2 Neural Networks for object detection (Tensor Cores are used)
OpenCL for Nets - A Deep Learning Framework based on OpenCL, written by C++. Supports popular MLP, RNN(LSTM), CNN(ResNet). Friendly debugger. Transparent data. No library dependencies. 基于OpenCL的深度学习计算框架,C++开发,支持多层感知器,长短时记忆模型,卷积神经网络,残差网络。调试方便,数据透明。无外部依赖。
Mobilenet v1 (3,160,160, alpha=0.25, and 3,192,192, alpha=0.5) on STM32H7 using X-CUBE-AI v4.1.0
deep learning convolutional neural network implemented with SIMD acceleration (auto-vectorization)
Mobilenet v1 trained on Imagenet for STM32 using extended CMSIS-NN with INT-Q quantization support
CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
Deep neural network. Uses CUDA with cuDNN and cuBLAS_v2 libraries. Provides flexible model building. As an example, classificates cell Images for detecting malaria.
Cortex M KWS example with Tengine Lite.
A Cross Platform Convolution Neural Network Library
Football players detection and tracking
Autonomous remote-controlled car using Deep Learning
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