wuzerun-888 Goto Github PK

deeplearning-mindmap

A mindmap summarising Deep Learning concepts.

deeplearning-weekly

Collect the most interesting deep learning（CV） applications, papers, and code for everyone！

deoldify

A Deep Learning based project for colorizing and restoring old images (and video!)

desktopassistant

A Virtual Desktop Assistant Written in Python

detectandtrack

The implementation of an algorithm presented in the CVPR18 paper: "Detect-and-Track: Efficient Pose Estimation in Videos"

disco-diffusion

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.

dress-code

Dress Code: High-Resolution Multi-Category Virtual Try-On. ECCV 2022

easymocap

Make human motion capture easier.

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

fastai

The fastai deep learning library

fastbook

The fastai book, published as Jupyter Notebooks

fastgpt

FastGPT is a knowledge-based question answering system built on the LLM. It offers out-of-the-box data processing and model invocation capabilities. Moreover, it allows for workflow orchestration through Flow visualization, thereby enabling complex question and answer scenarios!

fate

An Industrial Grade Federated Learning Framework

ffmpeg

Mirror of https://git.ffmpeg.org/ffmpeg.git

ffmpeg-build-script

The FFmpeg build script provides an easy way to build a static FFmpeg on OSX and Linux with non-free codecs included.

fisheye

fisheye image calibration

fisheyeplayer-for-vs2019

双鱼眼全景拼接播放器, 支持图片和视频实时拼接和播放, 支持rtmp, rtsp, hls等流媒体, 支持实时保存拼接后的结果

gan

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

get3d

gpt-2

Code for the paper "Language Models are Unsupervised Multitask Learners"

gpt-3

GPT-3: Language Models are Few-Shot Learners

graphneuralnetwork

《深入浅出图神经网络：GNN原理解析》配套代码

hands-on-gpu-programming-with-python-and-cuda

Hands-On GPU Programming with Python and CUDA, published by Packt

haohaopano

panoramic image mosaic

hr-viton

Official PyTorch implementation for the paper High-Resolution Virtual Try-On with Misalignment and Occlusion-Handled Conditions (ECCV 2022).

hyperpose

Library for Fast and Flexible Human Pose Estimation

image_sdr_to_hdr

图像SDR to HDR，目标：暗光区域 光照增强，过曝光区域 光照减弱

instant-ngp

Instant neural graphics primitives: lightning fast NeRF and more