jaxony/ShuffleNet
ShuffleNet in PyTorch. Based on https://arxiv.org/abs/1707.01083
This helps mobile app developers integrate efficient image classification directly into their applications. It takes raw image data as input and outputs classifications, identifying objects or features within those images, specifically designed to run quickly on mobile devices. It's for developers building AI-powered mobile features who need to minimize processing time and battery usage.
295 stars. No commits in the last 6 months.
Use this if you are a mobile developer building an application that needs to perform real-time image recognition or classification directly on a user's device, requiring high efficiency and low computational overhead.
Not ideal if you need to train or run extremely complex, high-accuracy models that don't prioritize mobile-first performance, or if you're not comfortable working with PyTorch for model integration.
Stars
295
Forks
90
Language
Python
License
MIT
Category
Last pushed
Dec 20, 2017
Commits (30d)
0
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