312 points by deeplearning_enthusiast 6 months ago flag hide 16 comments
deeplearning_fan 6 months ago next
Fascinating read! I'm impressed by the progress in neural network architectures for image recognition tasks. It's exciting to see researchers pushing the boundaries of efficiency in deep learning models.
johndoe 6 months ago next
Absolutely!This article provides a great overview of various techniques to improve the trade-off between recognition accuracy and computation efficiency. Efficient models that can run on mobile devices and edge compute platforms are essential for gaining wider adoption of deep learning capabilities throughout the industry.
mlengineer 6 months ago prev next
I've been using the EfficientNet architectures myself, and the results are indeed impressive. I'm curious though, how was the performance of the MobileNet v3 for image recognition?
deeplearning_fan 6 months ago next
MobileNet v3 also demonstrates excellent performance relative to the number of parameters and computational complexity. It's well-suited for mobile and embedded applications where resource constraints are critical. It's just one of the tools showcased in this article.
ai_fan 6 months ago prev next
In our research, GhostNet and ShuffleNet have provided a solid balance between computational and memory efficiency for resource-constrained devices in comparison to MobileNet v3. What's your experience with them for image recognition tasks?
mlengineer 6 months ago next
I'd love to see a benchmark or comparison between ResNet, DenseNet, GhostNet, ShuffleNet and SqueezeNet architectures in the image recognition domain. It could highlight the practical implications of computational complexity with real-world implementations.
mlengineer 6 months ago next
I'm curious about the memory footprint and runtime performance on edge devices for these architectures. Is there any specific study or benchmark you're aware of that looks into those factors and edge device implementation challenges?
mlengineer 6 months ago prev next
Another interesting architecture that is missing from the article is Single-Path Networks (SPN). They are relatively new to the scene but have shown very promising results, especially for low latency applications.
deeplearning_fan 6 months ago next
True, Single-Path Networks are an interesting alternative path and indeed provide low-latency advantages. They are worth investigating and comparing against the other architectures mentioned in the article for specific use-cases.
johndoe 6 months ago prev next
Unfortunately, as much as I would like to see an extensive comparison of all architectures mentioned, the article's focus is mainly on recent advancements rather than a comprehensive benchmark. But your resources would certainly be useful for a follow-up study or article!
hackerjane 6 months ago prev next
I've been following recent developments in the computer vision domain, and I have found that novel architectures like FBNet are interesting to explore. They offer a promising approach for deploying machine learning models on diverse hardware platforms. Does this article discuss FBNet or similar networks?
johndoe 6 months ago next
Well spotted! The FBNet architecture and optimization method are mentioned, along with the ESPNet, DenseNet, and ResNet for comparison. SqueezeNet was not included this time but is worth noting for its pioneering role in making deep learning models more resource-efficient.
hackerjane 6 months ago next
Indeed, SqueezeNet triggered much of this recent interest in finding better ways to design and deploy deep learning models. Thanks for the clarification. I appreciate your contributions to this already informative thread.
ai_fan 6 months ago next
Yes, smaller and efficient neural networks simplify edge deployment and human-interaction applications which is exactly the trend we are experiencing now. Benefits can be huge both in terms of UX and revenue for the industry.
deeplearning_fan 6 months ago next
I've been researching with GhostSqueezeNet and ShuffleSqueezeNet, and they have shown some improvement in edge device deployment compared to the original architectures. I anticipate their performance will continue to improve with more developments.
hackerjane 6 months ago next
I second the curiosity about memory and performance benchmarks for edge devices. A more collaborative research initiative from the community will help to progress and focus on the practical challenges involved in deploying deep learning models in real-world settings.