Open Models for Detection and Segmentation

4M is a framework for researchers and developers who want to train and run models that combine images, text and other visual information. A single model can perform vision tasks and generate images from mixed or partial inputs.

Detectron2 is a Python library for developers and researchers building object detection and image segmentation applications. It provides model training tools and reusable components for computer vision research and production software.

StarDist detects individual cells and nuclei in crowded 2D microscopy images and 3D volumes. It's for researchers and bioimage analysts who need separate outlines for closely packed objects. Its shape-based approach suits objects that can be represented by star-convex polygons in 2D or polyhedra in 3D.

SAM 3D Objects reconstructs objects in 3D from a single image. It's for researchers and developers who want to recover object geometry and appearance from photographs, including cluttered scenes with partly hidden objects.

Moondream is a vision language model for developers building apps that interpret images, such as warehouse or manufacturing footage. Its downloadable models can run on your own hardware, including offline in an air-gapped environment.

VoxelMorph is a Python library for researchers and developers who need to align medical images using learned deformations. It provides registration networks and training components, including unsupervised methods, with PyTorch and TensorFlow backends. The code is open source under the Apache 2.0 license.

FastSurfer processes structural human brain MRI scans on your own workstation or server, producing brain segmentations and cortical measurements compatible with FreeSurfer. It's for neuroimaging researchers working with individual scans or large cohorts who want shorter processing times within existing analysis workflows. The software is open source under Apache 2.0.

Cellpose is local AI software for biologists and microscopy researchers who need to identify individual cells and nuclei in images. It runs on Windows, Linux and macOS, with a graphical interface for automatic segmentation, manual annotation and correcting model results.

DeepLabCut is an open-source toolbox for researchers who want to track animal movement and behavior on their own computers without attaching physical markers. It estimates the positions of visible features you choose, with support for animals, humans and objects rather than a fixed set of species or body parts.

Segment Anything 2 (SAM 2) is Meta's model for creating object masks in images and videos. It's for developers and researchers building visual applications or annotating footage, with interactive selection and correction.

TorchGeo is an open source Python library for machine learning researchers and remote sensing specialists working with satellite and aerial imagery. It brings geographic datasets and pretrained models into PyTorch training workflows. The MIT license allows you to use and adapt it for your own projects.

HD-BET uses neural networks to separate brain tissue from surrounding structures in MRI scans. It runs locally and is aimed at researchers and medical imaging teams who need automated brain masks, including for scans affected by tumors, lesions or surgical resection cavities.

InsightFace is a face and person analysis toolkit for developers building identity verification, access control and media applications. Its Python tools and recognition server can process data on your own hardware.

Depth Anything estimates depth from ordinary images for developers and researchers who need depth maps without a depth camera. Its pretrained models show which parts of a scene are nearer or farther away; separate fine-tuned models estimate metric depth.

PaddleSeg is an image segmentation toolkit for developers building computer vision applications on their own hardware. It covers annotation, training and deployment for tasks such as medical image analysis, industrial inspection and portrait effects.

PaddleOCR is an OCR and document parsing toolkit for developers turning PDFs and images into data for document search, RAG systems and AI agents. Its local models cover both structured pages and text found in photographs.

InternVL is a family of vision-language models for developers and researchers building AI applications that interpret images and answer questions about them. Downloadable models let teams host inference themselves.

BiRefNet is an AI image segmentation model for developers and image workflow builders who need to separate subjects from backgrounds. It produces foreground masks for cutouts and has dedicated models for matting, where edges can include partial transparency.

Segment Anything for Microscopy adapts Meta AI's Segment Anything to biomedical images, with models fine-tuned on microscopy data. It's for researchers who need to label cells or structures such as mitochondria in electron microscopy volumes. The software runs locally on Windows, macOS and Linux and is open source under the MIT license.

Florence-2 is Microsoft's vision model for developers building applications that describe images, extract text or locate objects on their own hardware. One model handles several image tasks selected through text prompts.

PaddleDetection is a computer vision toolkit for developers building image and video analysis systems. It covers training, adapting and deploying models for uses such as inspection and traffic analysis.

DeepCell is an open-source Python library for researchers analyzing individual cells in biological images. It runs locally and uses TensorFlow to identify whole cells or nuclei and track cells through time-lapse movies. Its Apache 2.0 license allows users to adapt the library for their own research.

Grounding DINO locates objects in images from category names or descriptive text. It's for developers and computer vision researchers building detection or annotation workflows that need labels beyond a fixed category list.

CLIP is an image and text model for developers and researchers who want to classify pictures without building a labeled dataset for each task. You define the categories in natural language, and the model scores how closely each description matches an image.

HAT Super Resolution is a local AI image upscaling project for developers, researchers, and people working with low-resolution images. It provides pretrained Hybrid Attention Transformer models and code for training your own.

MedSAM is an open-source model for medical image segmentation that runs locally, aimed at researchers and developers working with medical scans. It builds on Meta's Segment Anything model and uses bounding boxes to identify the region you want to segment. The project uses the Apache 2.0 license and supports macOS.

DINOv3 is Meta AI's family of vision foundation models for researchers and developers building image analysis systems on their own hardware. Its pretrained models extract features for individual image regions that can support multiple vision tasks without fine-tuning the backbone.

Depth Pro estimates distances from a single image for researchers and developers who need depth maps with an absolute scale. It can make predictions without camera metadata, so images don't need to come with known camera intrinsics.

Ultralytics YOLO is a Python computer vision library for developers building applications that interpret images and video. Models can run on hardware you control for tasks such as visual inspection, inventory monitoring and robotics perception.

Lightning Pose is an open-source package for researchers who estimate animal poses from video for behavior analysis. It supports 2D and 3D pose estimation with single-camera and multiple-camera datasets, including footage where body parts become hidden from view.

PaddleClas is a PaddlePaddle toolkit for developers and researchers building image classification and recognition applications on their own hardware. It covers fixed-category classifiers and image matching for large or frequently changing catalogs.