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Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
The main features of paddlepaddle/paddleseg are: Computer Vision, Segmentation Tools and Utilities, Image segmentation.
Projects with overlapping indexed features include: ultralytics/ultralytics — Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep… open-mmlab/mmsegmentation — MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable… facebookresearch/segment-anything — This project provides a deep learning architecture designed to identify and isolate distinct objects within images by… ailab-cvc/yolo-world — YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images… albu/albumentations — Albumentations is an image augmentation library and computer vision preprocessing tool designed to expand datasets for… alankbi/detecto — Build fully-functioning computer vision models with PyTorch.
MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi
Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in
This project provides a deep learning architecture designed to identify and isolate distinct objects within images by generating precise pixel-level masks. It functions as a browser-based inference engine, enabling the execution of complex machine learning models directly within web environments without requiring server-side processing. The system distinguishes itself by utilizing hardware-accelerated execution and parallel processing to achieve real-time segmentation speeds. It supports prompt-based mask decoding, allowing users to generate spatial masks by providing specific points or boxes
YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on