ShichenLiu/CondenseNet
CondenseNet: Light weighted CNN for mobile devices
This project offers a method to build highly efficient image recognition models that can run on devices with limited computational power, such as mobile phones or embedded systems. It takes raw image data and processes it into classification results, such as identifying objects or faces. This is for engineers or product managers who need to deploy robust image recognition features in resource-constrained environments.
691 stars. No commits in the last 6 months.
Use this if you need to deploy advanced image classification or object recognition capabilities on mobile devices or other hardware with limited processing power and memory.
Not ideal if you are solely focused on achieving the absolute highest accuracy without any concern for model size or inference speed.
Stars
691
Forks
130
Language
Python
License
MIT
Category
Last pushed
Nov 11, 2019
Commits (30d)
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