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101crawlersweb

3d-mininet

Official Implementation in Pytorch and Tensorflow of 3D-MiniNet: Learning a 2D Representation from Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation

advent

Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

app_lensblur

centernet

Object detection, 3D detection, and pose estimation using center point detection:

classification_davis

classmix

code-plus-plus

colornet

Neural Network to colorize grayscale images

coral-segmentation

coralseg-learning-coral-segmentation-from-sparse-annotations

CoralSeg: Learning coral segmentation from sparse annotations

dcgan-keras-eager

deep-learning-models

Keras code and weights files for popular deep learning models.

deeplab-pytorch

PyTorch implementation of DeepLab v2 on COCO-Stuff / PASCAL VOC

deeplearning

A project focused on learning more stuff about deep learning using Keras.

densenet-tensorflow

Simple Tensorflow implementation of Densenet using Cifar10, MNIST

efects-openvc

efficientnet-pytorch

A PyTorch implementation of EfficientNet

erfnet_pytorch

Pytorch code for semantic segmentation using ERFNet

erfnet_segmentation

My own Implementation of the ERFNet Semantic Segmentation architecture in Tensorflow, trained on the CamvidDataset

ev-flownet

Code for the paper "EV-FlowNet: Self-Supervised Optical Flow for Event-based Cameras"

ev-segnet

Official Tensorflow implementation of Ev-SegNet

event-based-cameras

event-based-learning

event-based_vision_resources

fcn.berkeleyvision.org

Fully Convolutional Networks for Semantic Segmentation by Jonathan Long*, Evan Shelhamer*, and Trevor Darrell. CVPR 2015 and PAMI 2016.

fpconv

FPConv: Learning Local Flattening for Point Convolution, CVPR 2020

gan-example

googlehashcode18

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