Models

What is YOLOv7 Instance Segmentation?

YOLOv7 Instance Segmentation lets you perform segmentation tasks with the YOLOv7 model.

About the model

Here is an overview of the

YOLOv7 Instance Segmentation

model:

Date of Release
Model Type Instance Segmentation
Architecture
Framework Used PyTorch
Annotation Format YOLOv7 PyTorch TXT
Stars on GitHub 7900+

YOLOv7 was created by WongKinYiu and AlexeyAB, the creators of YOLOv4 Darknet (and the official canonical maintainers of the YOLO lineage according to pjreddie, the original inventor and maintainer of the YOLO architecture).

![YOLOv7 Instance Segmentation example](https://github.com/WongKinYiu/yolov7/blob/main)

YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)

Model: YOLOv7-seg
Test Size: 640
APbox: 51.4%
AP50box: 69.4%
AP75box: 55.8%
APmask: 41.5%
AP50mask: 65.5%
AP75mask: 43.7%

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Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Model Performance

Explore this model on Roboflow

YOLOv7 Instance Segmentation Annotation Format

YOLOv7 Instance Segmentation

uses the

YOLOv7 PyTorch TXT

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

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