Announcing Instance Segmentation Models on Matrice.ai
Jan 8, 2025
Our team at Matrice.ai is thrilled to share some big news: we’re integrating a brand-new suite of instance segmentation models into our platform! This expansion brings sophisticated image analysis capabilities to our fingertips, further strengthening our commitment to delivering cutting-edge AI solutions.
What is Instance Segmentation?
At a high level, instance segmentation is a computer vision task that not only detects objects within an image but also pinpoints the exact pixels associated with each object. Instead of merely identifying a bounding box, instance segmentation “highlights” every individual object—making it easier to perform tasks such as measuring object boundaries, area, and even shape attributes.
This enhanced level of visual understanding is integral to advanced AI applications in fields like:
Robotics
Healthcare
Autonomous driving
And more!
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How is Instance Segmentation Different from Object Detection?
Object detection finds and draws simple bounding boxes around objects. It answers the “what” and “where” by labeling objects and indicating their location with a rectangular border. However, if multiple objects are close together or overlapping, bounding boxes might not give a fine-grained perspective of the scene.
Instance segmentation, in contrast, creates more precise object masks. It goes beyond detecting that an object is within a box; it highlights the exact shape and boundaries of each object. This level of detail matters when you need a complete picture of how objects relate to one another or when you want accurate measurements of an object’s size and shape.
How Can People Use Matrice for Instance Segmentation?
Using Matrice for instance segmentation is incredibly intuitive. Once you’ve logged into the platform and accessed the dashboard, follow these simple steps:
Click the New Project button on the dashboard.
Fill in the necessary details in the project form.
Select “Instance Segmentation” as your project type.
Hit the “Create” button, and your instance segmentation project will be ready to go!
Next, upload your dataset in accordance with the dataset requirements. Once your dataset is uploaded, choose your preferred model from the available options and begin training. For step-by-step guidance, refer to the model training tutorial.
Key Features and Capabilities
Just like with classification and detection projects, Matrice allows you to:
Export a trained model: Easily download your trained instance segmentation model for use in your applications.
Deploy a trained model: Effortlessly deploy your model for inference tasks directly on the platform.
What Models Are Available Now, and What’s Coming in the Future?
Matrice currently supports two powerful model families for instance segmentation:
YOLOv8: Known for its speed and precision in various computer vision tasks.
YOLOv9: The latest iteration offering state-of-the-art performance.
Model Key |
Model Family |
Split Type |
Performance Metric |
Latency (ms) |
Parameters (millions) |
Benchmark Dataset |
|---|---|---|---|---|---|---|
yolov8n_seg |
YOLOv8_Instance_Segmentation |
val |
maskAP (30.5) |
302.57 |
3.40 |
COCO |
yolov8s_seg |
YOLOv8_Instance_Segmentation |
val |
maskAP (36.8) |
745.75 |
11.80 |
COCO |
yolov9c_seg |
YOLOv9_Instance_Segmentation |
val |
maskAP (42.2) |
1594.63 |
27.90 |
COCO |
yolov8m_seg |
YOLOv8_Instance_Segmentation |
val |
maskAP (40.8) |
607.32 |
27.30 |
COCO |
yolov8l_seg |
YOLOv8_Instance_Segmentation |
val |
maskAP (42.6) |
1992.76 |
46.00 |
COCO |
yolov9e_seg |
YOLOv9_Instance_Segmentation |
val |
maskAP (44.3) |
4095.39 |
60.50 |
COCO |
yolov8x_seg |
YOLOv8_Instance_Segmentation |
val |
maskAP (43.4) |
4094.58 |
71.80 |
COCO |
Looking ahead, we are working to integrate more cutting-edge models into our platform. One exciting addition on the horizon is EVA, a state-of-the-art segmentation model designed for even more accurate and efficient results.
Feel free to explore these new features and enhance your computer vision projects with Matrice.ai!
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