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bagan

Keras implementation of Balancing GAN (BAGAN) applied to the MNIST example.

cocoapi

COCO API - Dataset @ http://cocodataset.org/

cocosplit

Simple tool to split COCO annotations into train/test datasets.

cse481c

CSE 481C repository

deep-learning-in-production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

eccv-2020-patch-based-cnn-for-bark-classification

Paper accepted at the ECCV 2020 Workshop on Computer Vision Problems in Plant Phenotyping (CVPPP 2020)

ensembleobjectdetection

face-aging-cgan-with-pytorch

face_attention_network

Pytorch implementation of face attention network

faceforensics

Github of the FaceForensics dataset

fastai

The fastai deep learning library, plus lessons and and tutorials

fcn

PyTorch Implementation of Fully Convolutional Networks, for VGG and ResNet backbones.

federated-benchmark

A Benchmark of Real-world Image Dataset for Federated Learning

federated_object_detection

Federated Learning on object detection

few-shot-object-detection

Implementations of few-shot object detection benchmarks

few-shot-segmentation

PyTorch implementation of 'Squeeze and Excite' Guided Few Shot Segmentation of Volumetric Scans

fewshot_detection

Few-shot Object Detection via Feature Reweighting

gans-awesome-applications

Curated list of awesome GAN applications and demo

gans-collection.torch

Torch implementation of various types of GAN (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN, EBGAN, LSGAN)

graph-cut

Graph-cut Image Segmentation ---------------------------- Implements Boykov/Kolmogorov’s Max-Flow/Min-Cut algorithm for computer vision problems. Two gray-scale images have been used to test the system for image segmentation (foreground/background segmentation) problem. Steps: 1. defined the graph structure and unary and pairwise terms. For graph structure, i have used available packages/libraries such as PyMaxflow. 2. likelihood function for background and foreground has been generated. 3. General energy function consisting of unary and pairwise energy functionals have been written. 4. Likelihood maps (intensity map ranging from 0 to 1) for foreground and background have been displayed. 5. Use Boykov/Kolmogorov maxflow / mincut approach for solving the energy minimization problem. 6. Final segmentation have been displayed. Created an image for which the background pixels are red, and the foreground pixels have the color of the input image. Relevant paper can be found here: http://www.csd.uwo.ca/~yuri/Papers/pami04.pdf

graphcut

Graph cut image segmentation with custom GUI.

gym

A toolkit for developing and comparing reinforcement learning algorithms.

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hog-svm-python

homography-based-visual-odometry

Advanced Visual Geometry course lab assignment at Ecole Centrale de Nantes

hough_forests_object_detection

Object detection using Hough Forests

image-segmentation-keras

Implementation of Segnet, FCN, UNet , PSPNet and other models in Keras.

image-segmentation-using-genetic-algorithm

Project in class Computational Intelligence Methods

image-to-image-papers

🦓<->🦒 🌃<->🌆 A collection of image to image papers with code (constantly updating)