Summary of Rapidnet: Multi-level Dilated Convolution Based Mobile Backbone, by Mustafa Munir et al.
RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
by Mustafa Munir, Md Mostafijur Rahman, Radu Marculescu
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-Level Dilated Convolutions technique enables the development of a purely CNN-based mobile backbone, which outperforms state-of-the-art (SOTA) mobile CNN, ViT, ViG, and hybrid architectures in terms of accuracy and/or speed on image classification, object detection, instance segmentation, and semantic segmentation. This breakthrough allows for larger theoretical receptive fields than standard convolutions, enabling interactions between short-range and long-range features in images. The RapidNet-Ti model, with 0.9 ms inference latency on an iPhone 13 mini NPU, achieves 76.3% top-1 accuracy on ImageNet-1K, surpassing MobileNetV2x1.4 (74.7% top-1 with 1.0 ms latency). This innovative work demonstrates that pure CNN architectures can excel when designed correctly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to make computer vision models run faster and better on mobile devices, like smartphones. They’ve come up with a special type of neural network called Multi-Level Dilated Convolutions that makes their model more powerful than others that combine different types of networks. This breakthrough allows for more accurate image recognition, object detection, and other tasks. The fastest version of their model can even run in just 0.9 milliseconds on an iPhone, which is really fast! Their work shows that sometimes the simplest approach can be the best. |
Keywords
» Artificial intelligence » Cnn » Image classification » Inference » Instance segmentation » Neural network » Object detection » Semantic segmentation » Vit
