A toolkit for making real world machine learning and data analysis applications in C++

YOLOv4 - Neural Networks for Object Detection (Windows and Linux version of Darknet )

MediaPipe is the simplest way for researchers and developers to build world-class ML solutions and applications for mobile, edge, cloud and the web.

YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite

The Python Code Tutorials

An Open Source Machine Learning Framework for Everyone

Most popular metrics used to evaluate object detection algorithms.

RetinaFace (Single-stage Dense Face Localisation in the Wild, 2019) implemented (ResNet50, MobileNetV2 trained on single GPU) in Tensorflow 2.0+. This is an unofficial implementation. With Colab.

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的同意

A hyperparameter optimization framework

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

An awesome semantic segmentation model that runs in real time

The C based gRPC (C++, Python, Ruby, Objective-C, PHP, C#)

Keras implementation of Deeplab v3+ with pretrained weights

Keras implementation of CoordConv for all Convolution layers

YoloV3 Implemented in Tensorflow 2.0

TensorFlow examples

🍻 Default formulae for the missing package manager for macOS

Models and examples built with TensorFlow

redis qps监控小工具

A Keras implementation of CenterNet with pre-trained model (unofficial)

A tensorflow2.0 implementation of the YOLOv1 paper https://arxiv.org/pdf/1506.02640

mutil-class focal loss implemented in keras

使用自己的数据集retraining DeeplabV3+

Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.

tensorflow lite compile minimal

Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow.

Yolo for Android and iOS - Mobile Deep Learning Object Detection in Realtime written in Kotlin and Swift