giantstonex7 Goto Github PK

adaptsegnet

Learning to Adapt Structured Output Space for Semantic Segmentation, CVPR 2018 (spotlight)

alpha-zero-general

A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more

anomaly_detection_on_metal_surface

This is an anomaly detection classification algorithm based on CNN based approach

automatic_speech_recognition

End-to-end Automatic Speech Recognition for Madarian and English in Tensorflow

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

bittigerlab

沁原的硅谷创新课

cadencv

An optical music recognition system built as a final project for 6.819: Advances in Computer Vision course at MIT. Takes sheet music as input and returns a MIDI file of written music.

caffe

Caffe: a fast open framework for deep learning.

camautofocus

Computer Vision - Camera Auto-focusing System

conditional-pixelcnn-decoder

Tensorflow implementation of Gated Conditional Pixel Convolutional Neural Network

cvat

Powerful and efficient Computer Vision Annotation Tool (CVAT)

cyclegan

Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.

daily-interview

Datawhale成员整理的面经，内容包括机器学习，CV，NLP，推荐，开发等，欢迎大家star

deblurgan

Image Deblurring using Generative Adversarial Networks

deep-image-matting

Deep Image Matting

deep-image-matting-pytorch

Deep Image Matting implementation in PyTorch

deep-visual-constraints

Pytorch implimentation of the paper: "Deep Visual Constraints: Neural Implicit Models for Manipulation Planning from Visual Input"

deepinversion

deeplearning_tutorials

The deeplearning algorithms implemented by tensorflow

deepnude-an-image-to-image-technology

Research techniques and papers related to image generation and image restoration used by DeepNude. 研究DeepNude使用的图像生成和图像修复相关的技术和论文。

django-recommends

A django app that builds item-based suggestions for users.

easyocr

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

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.

face-aging-caae

Age Progression/Regression by Conditional Adversarial Autoencoder

faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

fast-style-transfer

TensorFlow CNN for fast style transfer! ⚡🖥🎨🖼

fastmcd

implementation of the paper "Detection of Moving Objects with Non-stationary Cameras in 5.8ms: Bringing Motion Detection to Your Mobile Device", CVPRW13

fastnerf

gan-image-similarity

InfoGAN inspired neural network trained on zap50k images (using Tensorflow + tf-slim). Intermediate layers of the discriminator network are used to do image similarity.

gcodeoptimizer