Image Segmentation
Showing 31 of 31 projects
A state-of-the-art PyTorch-based computer vision model for object detection, segmentation, and classification.
A PyTorch-based platform for state-of-the-art object detection, segmentation, and visual recognition tasks.
A fast and flexible Python library for image augmentation in computer vision tasks like classification, segmentation, and object detection.
A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
A curated list of semantic segmentation papers, code, datasets, and resources across various deep learning frameworks.
A comprehensive resource of deep learning techniques and models for analyzing satellite and aerial imagery.
A large-scale dataset of images with object segmentation, bounding boxes, and visual relationship annotations.
An open-source deep learning API and server written in C++ that supports multiple backends like PyTorch, TensorRT, and TensorFlow for training and inference.
A web/desktop application for collaborative labeling and annotation of images, text, audio, documents, and other data types.
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A deep learning-based edge detection algorithm using holistically-nested fully convolutional neural networks.
An open-source C library with MATLAB interfaces implementing popular computer vision algorithms for image understanding and local feature extraction.
An all-in-one framework for training state-of-the-art computer vision models, covering pretraining, fine-tuning, and distillation.
A fast, modular PyTorch reference implementation for training and evaluating semantic segmentation models.
A curated collection of open-source computer vision pre-trained models across TensorFlow, Keras, PyTorch, Caffe, and MXNet frameworks.
Interactive segmentation and tracking tools for microscopy images built on Segment Anything.
A U-Net implementation for brain tumor segmentation using the BRATS 2017 dataset with data augmentation and dice loss.
A Python toolbox for image segmentation featuring superpixel segmentation, object center detection, and region growing with shape priors.
A neural networks toolbox for medical image analysis, providing specialized layers, models, and utilities for TensorFlow/Keras.
FLAME dataset and deep learning models for fire detection in aerial imagery using UAVs, supporting classification and segmentation tasks.
A semi-automated pipeline for instance-aware cell segmentation, tracking, and migration analysis in phase contrast microscopy using Mask R-CNN.
A PyTorch-based segmentation toolbox for electron microscopy connectomics, enabling neural structure analysis in 3D volumes.
A public dataset of field images with segmentation masks and plant type annotations for computer vision in precision agriculture.
Open-source implementation of the winning solution for the 2018 Data Science Bowl Kaggle competition using PyTorch and U-Net.
A deep learning tool for automatic axon and myelin segmentation from microscopy images using convolutional neural networks.
A Fiji plugin for pixel-based image segmentation using Weka machine learning algorithms and image features.
A tool for cell instance aware segmentation in densely packed 3D volumetric images, originally developed for plant tissues.
An open-source benchmark solution for the Kaggle TGS Salt Identification Challenge using semantic segmentation.
A Python package providing popular computer vision model architectures built with Equinox for JAX.
R package for segmentation, registration, and web-based atlas generation from microscope brain images.
A GUI-based tool for training deep neural networks to segment biological images using corrective annotation.
An open-source solution for the Airbus Ship Detection Challenge, providing a benchmark and base for ship detection in satellite imagery.
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