SoltStreaming over lightweight data transformations
Pixel level land classificationTutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
PyimsegmImage segmentation - general superpixel segmentation & center detection & region growing
Signature extractorA super lightweight image processing algorithm for detection and extraction of overlapped handwritten signatures on scanned documents using OpenCV and scikit-image.
DataturksML data annotations made super easy for teams. Just upload data, add your team and build training/evaluation dataset in hours.
Keras UnetHelper package with multiple U-Net implementations in Keras as well as useful utility tools helpful when working with image semantic segmentation tasks. This library and underlying tools come from multiple projects I performed working on semantic segmentation tasks
PiccanteThe hottest High Dynamic Range (HDR) Library
DeepdetectDeep Learning API and Server in C++14 support for Caffe, Caffe2, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
Hms Ml DemoHMS ML Demo provides an example of integrating Huawei ML Kit service into applications. This example demonstrates how to integrate services provided by ML Kit, such as face detection, text recognition, image segmentation, asr, and tts.
OsmdeepodOSMDeepOD - OpenStreetMap (OSM) and Machine Learning (Deep Learning) based Object Detection from Aerial Imagery (Formerly also known as "OSM-Crosswalk-Detection").
Kaggle dstl submissionCode for a winning model (3 out of 419) in a Dstl Satellite Imagery Feature Detection challenge
Super BpdSuper-BPD: Super Boundary-to-Pixel Direction for Fast Image Segmentation (CVPR 2020)
P2palaPage to PAGE Layout Analysis Tool
LivianetThis repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"
Tf unetGeneric U-Net Tensorflow implementation for image segmentation
HyperdensenetThis repository contains the code of HyperDenseNet, a hyper-densely connected CNN to segment medical images in multi-modal image scenarios.
LabelboxLabelbox is the fastest way to annotate data to build and ship computer vision applications.
Trimap generatorGenerating automatic trimap through pixel dilation and strongly-connected-component algorithms
Universal Data ToolCollaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
Tta wrapperTest Time image Augmentation (TTA) wrapper for Keras model.
Fast Slic20x Real-time superpixel SLIC Implementation with CPU
Sky DetectorSky area detection without deep neural networks https://maybeshewill-cv.github.io/sky-detector/
Tf SegnetSegNet-like network implemented in TensorFlow to use for segmenting aerial images
PaddlesegEnd-to-end image segmentation kit based on PaddlePaddle.
Keras IcnetKeras implementation of Real-Time Semantic Segmentation on High-Resolution Images
Attention Gated NetworksUse of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
Multiclass Semantic Segmentation CamvidTensorflow 2 implementation of complete pipeline for multiclass image semantic segmentation using UNet, SegNet and FCN32 architectures on Cambridge-driving Labeled Video Database (CamVid) dataset.
Coco Annotator✏️ Web-based image segmentation tool for object detection, localization, and keypoints
SegmentationcppA c++ trainable semantic segmentation library based on libtorch (pytorch c++). Backbone: ResNet, ResNext. Architecture: FPN, U-Net, PAN, LinkNet, PSPNet, DeepLab-V3, DeepLab-V3+ by now.
AlbumentationsFast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Graph Based Image SegmentationImplementation of efficient graph-based image segmentation as proposed by Felzenswalb and Huttenlocher [1] that can be used to generate oversegmentations.
Midv 500 ModelsModel for document segmentation trained on the midv-500-models dataset.
Wbc segmentaionWhite Blood Cell Image Segmentation Using Deep Convolution Neural Networks