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YOLOv7 Instance Segmentation

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

Try the Model

Use the widget below to experiment with YOLOv7 Instance Segmentation. You can detect COCO classes such as people, vehicles, animals, household items.

Overview

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 supports real-time vision, giving it several use cases. The model also is flexible in terms of export formats, where it supports ONNX and TensorRT, giving it seemless integration to hardware devices.

Learn how to deploy your own YOLOv7 model.

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Benchmarks YOLOv7 for instance segmentation

On a test size of 640, YOLOv8 Instance Segmentation has the following accuracies on the stated benchmarks.
APbox: 51.4%
AP50box: 69.4%
AP75box: 55.8%
APmask: 41.5%
AP50mask: 65.5%
AP75mask: 43.7%

YOLOv7 Instance Segmentation License

YOLOv7 Instance Segmentation

is licensed under a

GPL-3.0

license.

Performance

Deploy a YOLOv7 Instance Segmentation API

You can use Roboflow Inference to deploy a

YOLOv7 Instance Segmentation

API on your hardware. You can deploy the model on CPU (i.e. Raspberry Pi, AI PCs) and GPU devices (i.e. NVIDIA Jetson, NVIDIA T4).

Below are instructions on how to deploy your own model API.

You can run fine-tuned YOLOv7 instance segmentation models with Inference.

First, install Inference:‍


pip install inference

Retrieve your Roboflow API key and save it in an environment variable called ROBOFLOW_API_KEY:


export ROBOFLOW_API_KEY="your-api-key"

To use your model, run the following code:


import inference
model = inference.load_roboflow_model("model-name/version")
results = model.infer(image="YOUR_IMAGE.jpg")

Above, replace:

YOUR_IMAGE.jpg with the path to your image.
model_id/version with the YOLOv7 model ID and version you want to use. Learn how to retrieve your model and version ID

Label Data Automatically with YOLOv7 Instance Segmentation

You can automatically label a dataset using YOLOv7 Instance Segmentation with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use YOLOv7 Instance Segmentation to train a computer vision model.

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YOLOv7 Instance Segmentation Alternatives

Compare YOLOv7 Instance Segmentation Model Sizes

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Popular YOLOv7 Instance Segmentation Use Cases

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YOLOv7 Instance Segmentation Integrations

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YOLOv7 Instance Segmentation

Try the Model

Overview

Benchmarks YOLOv7 for instance segmentation

YOLOv7 Instance Segmentation License

Performance

Deploy a YOLOv7 Instance Segmentation API

Label Data Automatically with YOLOv7 Instance Segmentation

YOLOv7 Instance Segmentation Alternatives

Compare YOLOv7 Instance Segmentation Model Sizes

Deploy to Production

Build a Workflow

Data

Training

Utilities

Compare to Related Models

Convert Annotation Format

Architecture

Frameworks

Annotation Format

Popular YOLOv7 Instance Segmentation Use Cases

YOLOv7 Instance Segmentation Integrations

Explore This Model on Roboflow

How to Train Detectron2 for Custom Instance Segmentation

How to Train Detectron2 for Custom Instance Segmentation

Launch: Instance Segmentation Project Training and Inference

Launch: Instance Segmentation Project Training and Inference

YOLOv7 - A breakdown of how it works

What is Instance Segmentation? A Guide.

What is Instance Segmentation? A Guide.

How to Create Segmentation Masks with Roboflow

How to Train YOLOv5 Instance Segmentation with Custom Data

How to Train YOLOv5 Instance Segmentation with Custom Data

How to Train YOLOv7 Instance Segmentation on a Custom Dataset

How to Train YOLOv7 Instance Segmentation on a Custom Dataset

How to Train YOLOv7 Instance Segmentation on a Custom Dataset

Difference Between Semantic and Instance Segmentation

Difference Between Semantic and Instance Segmentation

How to Deploy YOLOv7 to Jetson Nano

Semantic Segmentation for Labeling, Training, Deployment

How to Train YOLOv7 on a Custom Dataset

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