YOLOv8s-Segmentation-INT8
Semantic Segmentation
INT8
post
YOLOv8s-Segmentation: Semantic Segmentation

YOLOv8s-Segmentation is one of the latest versions of the YOLO series, focusing on object detection and instance segmentation tasks. It combines YOLOv8's efficient object detection capabilities with instance segmentation, allowing for precise object boundary localization and segmentation within images. Compared to previous YOLO versions, YOLOv8-Seg features architectural improvements that enhance accuracy and speed for segmentation tasks. The model incorporates advanced convolutional neural network designs, with deeper feature extraction networks and efficient inference mechanisms, making it highly effective in real-time segmentation tasks. YOLOv8-Seg is widely used in applications like autonomous driving, medical image analysis, and video surveillance, offering a powerful solution for instance segmentation.

Source model

  • Input shape: 640x640
  • Number of parameters: 11.27M
  • Model size: 45.22M
  • Output shape: 1x32x160x160, 1x116x8400

Source model repository: YOLOv8-Seg

Performance Reference
Device
Backend
Precision
Inference Time
Accuracy Loss
File Size
Model Optimization

When the user has fine-tuned the source model, the model conversion process must be performed again.

Users can refer to either of the following two methods to complete the model conversion:

  • Using AIMO for model conversion: Click Model Conversion Reference in the Performance Reference section on the right to view the conversion steps.

  • Using Qualcomm QNN for model conversion: Please refer to the Qualcomm QNN Documentation.

Model Inference

The model performance benchmarks and example code provided by Model Farm are all implemented based on the APLUX AidLite SDK.

For models in .bin format, you can use either of the following two inference engines to run inference on Qualcomm chips:

Inference using APLUX AidLite: please refer to the APLUX AidLite Developer Documentation

Inference using Qualcomm QNN: Please refer to the Qualcomm QNN Documentation

Inference Example Code

The inference example code is implemented using the AidLite SDK.

Click Model & Code to download the model files and the inference code package. The file structure is as follows:

/model_farm_{model_name}_aidlite
    
    |__ models # folder where model files are stored

    |__ python # aidlite python model inference example

    |__ cpp # aidlite cpp model inference example

    |__ README.md
License
Source Model:AGPL-3.0
Deployable Model:AGPL-3.0
Performance Reference
Device
Backend
Precision
Inference Time
Accuracy Loss
File Size