AmirMansurian/AICSD
Adaptive Inter-Class Similarity Distillation for Semantic Segmentation (MTAP 2025)
This project helps computer vision engineers develop more efficient image segmentation models. It takes a larger, high-performing segmentation model and a smaller, less complex model as input. The output is a smaller model that achieves significantly improved segmentation accuracy, making it more suitable for deployment in resource-constrained environments. Computer vision practitioners focused on deploying models for image analysis would benefit from this.
Use this if you need to deploy an image segmentation model to devices with limited computational power, such as mobile phones or embedded systems, without sacrificing too much accuracy.
Not ideal if your primary goal is to achieve the absolute highest segmentation accuracy regardless of model size or computational efficiency.
Stars
29
Forks
7
Language
Python
License
—
Category
Last pushed
Nov 14, 2025
Commits (30d)
0
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