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awesome-computer-vision

A curated list of awesome computer vision resources

awesome-public-datasets

An awesome list of high-quality open datasets in public domains (on-going).

batchgenerators

A framework for data augmentation for 2D and 3D image classification and segmentation

caltech-tutorial

Material for the Caltech tutorial on deep learning and tensor methods

co

An elegant and efficient C++ basic library for Linux, Windows and Mac.

code

Code for the book "Mastering OpenCV with Practical Computer Vision Projects" by Packt Publishing 2012.

codeepneat

An implementation of CoDeepNEAT with possible extensions

confusion

Code for the ECCV 2018 paper "Pairwise Confusion for Fine-Grained Visual Classification"

cpp_study

follow me to study modern c++

cv_python

Some Project of Computer Vision, using opencv and python

deep_learning

dl_utils

some utils for training images

drml_pytorch

Implementation of Deep Region and Multi-Label Learning for Facial Action Unit Detection.

emotion-recognition-neural-networks

Emotion recognition using DNN with tensorflow

faster-rcnn_tf

Faster-RCNN in Tensorflow

fastmaskrcnn

Mask RCNN in TensorFlow

fpn

Feature Pyramid Networks for Object Detection

keras-frcnn

machine-learning-notes

My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(1000+页)和视频链接

medicalzoopytorch

MedicalZooPytorch: A pytorch-based deep learning framework for multi-modal 2D/3D medical image segmentation

monai

AI Toolkit for Healthcare Imaging

mooc_os_lab

OS Labs for MOOC

nonrigidicp

Copy of http://www.mathworks.com/matlabcentral/fileexchange/41396-nonrigidicp

openface

OpenFace – a state-of-the art tool intended for facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation.

openlongtailrecognition-oltr

Pytorch implementation for "Large-Scale Long-Tailed Recognition in an Open World" (CVPR 2019 ORAL)

oxford_dl

The assignment for deep learning course by Oxford in 2015

pix2text

Pix In, Latex & Text Out. Recognize Chinese, English Texts, and Math Formulas from Images.

pytorch-agent

Pytorch Agent is a high-level API that simplifies deep learning programming.

retina-unet

Retina blood vessel segmentation with a convolutional neural network

scalable-video-codec-for-diverse-networking-conditions

Video streaming is gaining more and more popularity due to video based applications, and due to heterogeneous networking environment,a codec is needed that can facilitate every user according to his requirement which should be user friendly also. So by using H.264 video compression standard, we have developed a scalable video codec for such networking conditions. Purpose of choosing H.264 for compression is its ability to compress the video at a rate twice than previous standards while maintaining the quality of video. The main idea is to achieve compression, maintaining the quality and integrity of video. Scalability is achieved in such a way that video is encoded just once decoded but it can be decoded multiple times according to the requirement of user. All the three modes of scalable codec are covered and implemented; temporal, spatial and quality scalability with better results. In temporal scalability, scalability is achieved by varying the frame rate, in spatial; by varying the resolution and in quality by varying the bit rate of the input video. All the three modes split the video into different layers and then scalable codec is applied over them. Scalable codec is tested over different videos and better results are obtained in terms of scalability and compression.