The Best 395 Image Segmentation Tools in 2025

CLIPSeg is an image segmentation model based on text and image prompts, supporting zero-shot and one-shot image segmentation tasks.

BRIA RMBG v1.4 is an advanced background removal model designed for efficiently separating foreground and background in various types of images, suitable for non-commercial use.

The latest background removal model developed by BRIA AI, capable of effectively separating foreground and background in various images, suitable for large-scale commercial content creation scenarios.

SegFormer model fine-tuned on ATR dataset for clothing and human segmentation

SAM is a vision model capable of generating high-quality object masks from input prompts (such as points or boxes), supporting zero-shot segmentation tasks

BiRefNet is a deep learning model for high-resolution binary image segmentation, which achieves accurate image segmentation through a bilateral reference network.

SegFormer is a Transformer-based semantic segmentation model fine-tuned on the ADE20K dataset, suitable for image segmentation tasks.

SAM is a visual model capable of generating high-quality object masks from input points or bounding boxes, with zero-shot transfer capability.

Semantic segmentation model fine-tuned from nvidia/mit-b5 for face parsing tasks

SAM is a vision model capable of generating high-quality object masks based on input prompts, supporting zero-shot transfer to new tasks

A large-scale Mask2Former model based on the Swin backbone network, specifically trained for Cityscapes semantic segmentation tasks, adopting a unified architecture for various image segmentation tasks.

A large-scale version based on the Swin backbone network, trained on the ADE20k semantic segmentation dataset, employing a unified paradigm for image segmentation tasks.

SAM 2 is a foundational model for promptable visual segmentation in images and videos developed by FAIR, supporting universal segmentation tasks through prompts.

SegFormer is a Transformer-based semantic segmentation model fine-tuned on the ADE20k dataset, suitable for 512x512 resolution image segmentation tasks.

This model performs simultaneous segmentation and classification tasks on chest X-rays, including lung/heart segmentation, position recognition, and age/gender prediction.

OneFormer is the first multi-task universal image segmentation framework, achieving semantic segmentation, instance segmentation, and panoptic segmentation tasks with a single model

A foundational model for promptable visual segmentation in images and videos developed by FAIR

A mini version of the Mask2Former instance segmentation model trained on the COCO dataset, utilizing the Swin backbone network to handle segmentation tasks uniformly

OneFormer is the first multi-task universal image segmentation framework that supports semantic segmentation, instance segmentation, and panoptic segmentation tasks with a single model.

BiRefNet is a high-resolution binary image segmentation model based on bilateral reference, specifically designed for high-resolution transparent image matting.

SegFormer model fine-tuned on the ATR dataset, primarily used for clothing segmentation and also applicable to human body segmentation

SegFormer is a Transformer-based semantic segmentation model featuring a hierarchical encoder and lightweight MLP decoder design, excelling in benchmarks like ADE20K and Cityscapes.

A fashion item image segmentation model based on SegFormer architecture, specifically designed for identifying and segmenting clothing and accessories

A multi-task universal image segmentation model trained on the Cityscapes dataset, supporting semantic segmentation, instance segmentation, and panoptic segmentation tasks

Mask2Former is a unified image segmentation framework capable of handling instance segmentation, semantic segmentation, and panoptic segmentation tasks. This model is based on the Swin-Tiny backbone network and has been fine-tuned for semantic segmentation on the Cityscapes dataset.

A series of object detection and segmentation models trained based on the YOLOv8 and YOLOv11 architectures, focusing on artistic image processing

The Mask2Former model based on the Swin backbone network, trained on the COCO panoptic segmentation dataset, adopts a unified paradigm to handle instance segmentation, semantic segmentation, and panoptic segmentation tasks.

SegFormer is a Transformer-based semantic segmentation model fine-tuned on the ADE20k dataset, suitable for image segmentation tasks at 512x512 resolution.

UperNet is a framework for semantic segmentation that uses ConvNeXt as its backbone network, enabling pixel-level semantic label prediction.

SegFormer is a Transformer-based semantic segmentation model fine-tuned on the ADE20k dataset, suitable for image segmentation tasks.

SAM 2 is a foundational model for promptable visual segmentation in images and videos developed by FAIR, supporting efficient segmentation through prompts.

A large-scale version of Mask2Former based on the Swin backbone network, specifically trained for panoptic segmentation tasks on the COCO dataset

Mask2Former is a Transformer-based unified image segmentation model, utilizing a Swin-Large backbone and fine-tuned on the COCO dataset, specializing in instance segmentation tasks.

BiRefNet is a bilateral reference framework model for high-resolution binary image segmentation, focusing on background removal and mask generation tasks.

A SegFormer semantic segmentation model fine-tuned on the CityScapes dataset at 1024x1024 resolution, featuring a hierarchical Transformer encoder and a lightweight all-MLP decoder head architecture.

AM-RADIO is a visual foundation model developed by NVIDIA Research, featuring an aggregated architecture for unified multi-domain representation, suitable for various computer vision tasks.

UperNet is a semantic segmentation framework combined with the ConvNeXt large backbone network for pixel-level semantic label prediction.

This SegFormer model is fine-tuned on the CityScapes dataset at 1024x1024 resolution, featuring a hierarchical Transformer encoder and lightweight all-MLP decoder head architecture.

SlimSAM is an innovative SAM model compression method that efficiently reuses pre-trained SAM through a unified pruning-distillation framework, eliminating the need for extensive repeated training.

SAM 2 is a foundational model for promptable visual segmentation in images and videos developed by FAIR, supporting efficient segmentation through prompts.

Mask2Former is a unified image segmentation model based on Transformer, fine-tuned on the COCO dataset for instance segmentation tasks

SegFormer is a Transformer-based semantic segmentation model. This version only includes the encoder pretrained on ImageNet-1k.