Search Results for "segmentation" - Page 5
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Showing 140 open source projects for "segmentation"
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Synonyms
Chinese synonyms, chat robot, intelligent question and answer toolkit
Chinese Synonyms for natural language processing and understanding. Better Chinese synonyms, chatbot, intelligent question and answer toolkit. synonymsCan be used for many tasks in natural language understanding, text alignment, recommendation algorithms, similarity calculation, semantic shifting, keyword extraction, concept extraction, automatic summarization, search engines, etc. Print synonyms in a friendly way for easy debugging. "Synonyms Cilin" was compiled by Mei Jiaju and others in... -
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PaddlePaddle models
Pre-trained and Reproduced Deep Learning Models
Pre-trained and Reproduced Deep Learning Models ("Flying Paddle" official model library, including a variety of academic frontier and industrial scene verification of deep learning models) Flying Paddle's industrial-level model library includes a large number of mainstream models that have been polished by industrial practice for a long time and models that have won championships in international competitions; it provides many scenarios for semantic understanding, image classification, target detection, image segmentation, text recognition, speech synthesis, etc. An end-to-end development kit that meets the needs of enterprises for low-cost development and rapid integration. The model library of Flying Paddle is an industrial-level model library tailored around the actual R&D process of domestic enterprises, serving enterprises in many fields such as energy, finance, industry, and agriculture. -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
...Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models (covering tasks such as Chinese word segmentation, named entity recognition, syntactic analysis, text classification, text matching, metaphor resolution, summarization, etc.). Trainer provides a variety of built-in Callback functions to facilitate experiment recording, exception capture, etc. Automatic download of some datasets and pre-trained models. -
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CC-Net
Tools to download and cleanup Common Crawl data
cc_net provides tools to download, segment, clean, and filter Common Crawl to build large-scale text corpora, including monolingual datasets and the multilingual CC-100 collection introduced in the associated paper. It includes pipelines to fetch snapshots, extract text, de-duplicate, identify language, and apply quality filtering based on heuristics and language models. The outputs are intended for pretraining language models and for creating standardized corpora that can be reproduced or... -
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GIMP ML
AI for GNU Image Manipulation Program
...It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on images such as edge detection and color clustering have also been added. GIMP-ML relies on standard Python packages such as numpy, scikit-image, pillow, pytorch, open-cv, scipy. ... -
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Tashkeela processed
Tashkeela dataset cleaned and normalized.
...The result is a space-separated tokens file, where the words and the numbers are separated, but not the sequences of punctuation (ie, an ending parenthesis followed by a dot). The sentence segmentation is done at usual punctuations such as dots, commas, interrogation/exclamation marks, and line end as well. The partition process is done by shuffling groups of sentences then dividing each group into three parts (Train/Val/Test) and storing them in individual files. The original Tashkeela dataset is available at https://sourceforge.net/projects/tashkeela/ -
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covid-chestxray-dataset
We are building an open database of COVID-19 cases with chest X-ray
To build a public open dataset of chest X-ray and CT images of patients who are positive or suspected of COVID-19 or other viral and bacterial pneumonia (MERS, SARS, and ARDS.). Data will be collected from public sources as well as through indirect collection from hospitals and physicians. All images and data will be released publicly in this GitHub repo. This project is approved by the University of Montreal's Ethics Committee #CERSES-20-058-D. We can extract images from publications. Help... -
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Frontend Regression Validator (FRED)
Visual regression tool used to compare baseline and updated instances
...The visual analysis computes the Normalized Mean Squared error and the Structural Similarity Index on the screenshots of the baseline and updated sites, while the visual AI looks at layout and content changes independently by applying image segmentation Machine Learning techniques to recognize high-level text and image visual structures. This reduces the impact of dynamic content yielding false positives. FRED is designed to be scalable. It has an internal queue and can process websites in parallel depending on the amount of RAM and CPUs (or GPUs) available. -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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COCO Annotator
Web-based image segmentation tool for object detection & localization
COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection. It provides many distinct features including the ability to label an image segment (or part of a segment), track object instances, label objects with disconnected visible parts, and efficiently store and export annotations in the well-known COCO format. The annotation process is delivered through an intuitive and... -
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imgaug
Image augmentation for machine learning experiments
...Scale segmentation maps, average/max pool of images/maps, pad images to aspect ratios (e.g. to square them). Draw heatmaps, segmentation maps, keypoints, bounding boxes, etc. -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
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jieba
Stuttering Chinese word segmentation
"Jaba" Chinese word segmentation, do the best Python Chinese word segmentation component. Four word segmentation modes are supported. Precise mode, which tries to cut the sentence most precisely, suitable for text analysis. Full mode, scans all the words that can be formed into words in the sentence, the speed is very fast, but the ambiguity cannot be resolved. -
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PyTracking
Visual tracking library based on PyTorch
A general python framework for visual object tracking and video object segmentation, based on PyTorch. Official implementation of the RTS (ECCV 2022), ToMP (CVPR 2022), KeepTrack (ICCV 2021), LWL (ECCV 2020), KYS (ECCV 2020), PrDiMP (CVPR 2020), DiMP (ICCV 2019), and ATOM (CVPR 2019) trackers, including complete training code and trained models. -
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Budou
Budou is an auto organizer tool for beautiful line breaking in CJK
...Budou addresses this issue by segmenting sentences into logical lexical chunks and wrapping each chunk in non-breaking HTML <span> tags. These spans can be styled with CSS to ensure smooth, visually coherent line breaks without splitting words or phrases. The tool supports multiple segmentation backends, including Google Cloud Natural Language API, MeCab, and TinySegmenter, enabling flexibility for both cloud-based and offline processing. Budou can be used via command line, in Python scripts, or integrated into web applications, and it provides advanced options such as caching and entity recognition for improved segmentation accuracy. -
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NiftyNet
An open-source convolutional neural networks platform for research
...Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging data. Quickly build new solutions to your own image analysis problems. NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use. -
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. The framework integrates critical components—region proposal networks (RPNs), RoIAlign layers, mask heads, and backbone architectures such as ResNet and FPN—optimized for both accuracy and speed. ... -
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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RefineNet
RefineNet: Multi-Path Refinement Networks
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. -
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mzitu
Python crawler that downloads image galleries and analyzes titles
...It focuses on automating the collection of large sets of images by programmatically parsing page content and iterating through gallery entries. mzitu also includes a simple analysis script that processes downloaded folder names to generate statistics and visualizations. Using text segmentation and frequency analysis, the project can create a word cloud representing common keywords found in the dataset. This makes the repository both a scraping example and a small data analysis experiment built around the collected content. Overall, mzitu serves as a learning-oriented implementation of Python web scraping, data processing, and visualization techniques. -
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DIGITS
Deep Learning GPU training system
The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. ... -
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pyhanlp
Chinese participle
...In practice, it serves as a bridge layer: Python calls are translated into the corresponding HanLP operations, so you can keep your application logic in Python while relying on HanLP’s implementations. It is especially useful when you need a pragmatic “get results quickly” NLP layer for segmentation, tagging, entity extraction, parsing, or keyword-style tasks rather than experimenting with model training from scratch. -
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House3D
A Realistic and Rich 3D Environment
...Each environment includes fully labeled 3D objects, allowing agents to perceive and interact with their surroundings through multiple sensory modalities including RGB images, depth maps, semantic segmentation masks, and top-down maps. The simulator is optimized for high-performance rendering, achieving thousands of frames per second to enable efficient large-scale training of RL agents. House3D has served as the foundation for several influential research projects such as RoomNav (for concept-based navigation) and Embodied Question Answering (EQA). -
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uncaptcha
Defeating Google's audio reCaptcha with 85% accuracy
uncaptcha is an open-source proof-of-concept system designed to demonstrate vulnerabilities in Google’s audio reCAPTCHA challenges by automatically solving them using speech recognition techniques. The project uses browser automation to navigate to CAPTCHA challenges, extract audio files, and process them through multiple speech-to-text services. By combining outputs from several transcription engines, the system increases the likelihood of correctly identifying the spoken digits or phrases... -
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Composight is a cross-platform toolkit for 3D-image processing in the domain of composite materials science. It is written in C++ and provides small, problem-specific applications for viewing, filtering and segmentation of volumetric data such as micro-CT scans. The main objective is not to provide yet another complex application for volume data visualization and medical image processing. Instead, Composight is a collection of small and simple apps that have already been successfully used to solve various problems in materials science.
