There are 15 repositories under dnn topic.
AiLearning:数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
Go package for computer vision using OpenCV 4 and beyond.
Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.
NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
DNN (formerly DotNetNuke) is the leading open source web content management platform (CMS) in the Microsoft ecosystem.
Voice activity detection (VAD) toolkit including DNN, bDNN, LSTM and ACAM based VAD. We also provide our directly recorded dataset.
Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving (ICCV, 2019)
仅使用numpy从头开始实现神经网络,包括反向传播公式推导过程; numpy构建全连接层、卷积层、池化层、Flatten层;以及图像分类案例及精调网络案例等,持续更新中... ...
Tutorial for computer vision and machine learning in PHP 7/8 by opencv (installation + examples + documentation)
An Open Source Modular Framework From Face to FACS Based Avatar Animation (Unity3D / Blender)
opencv 4.5+ with dnn module for php 7/8
Optimized (for size and speed) Caffe lib for iOS and Android with out-of-the-box demo APP.
Heterogeneous 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.
Deep-Learning based CTR models implemented by PyTorch
Android Voice Activity Detection (VAD) library. Supports WebRTC VAD GMM, Silero VAD DNN, Yamnet VAD DNN models.
An Open Source Deep Learning Inference Engine Based on FPGA
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))