iduta/iresnet
Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
This project offers an enhanced approach to image and video recognition tasks, improving accuracy without increasing the complexity or computational cost. It takes raw image and video data and classifies them with higher precision. This is useful for researchers and practitioners in computer vision who are building or evaluating deep learning models for visual content analysis.
249 stars. No commits in the last 6 months.
Use this if you are a machine learning researcher or engineer aiming to improve the classification accuracy of deep learning models for image and video recognition without adding more computational overhead.
Not ideal if you are looking for a pre-trained, plug-and-play solution for general image classification without needing to train or fine-tune models.
Stars
249
Forks
40
Language
Python
License
MIT
Category
Last pushed
Jul 16, 2022
Commits (30d)
0
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