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In this video I cover the recently published "A ConvNet for the 2020s" paper. They show that ConvNets are still in the game! - by adding new design ideas and training procedures they outperform vision transformers even in big data regimes and without any attention layers.
Convolutional prior continues to stand the test of time in the field of computer vision.
Note: I also partially cover the Swin transformer paper in case you missed out on that one. :)
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✅ Paper: https://arxiv.org/abs/2201.03545
✅ GitHub: https://github.com/facebookresearch/ConvNeXt
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⌚️ Timetable:
00:00 Intro - convergence of transformers and CNNs
05:05 Main diagram explained
07:40 Main diagram corrections
10:10 Swin transformer recap
20:20 Modernizing ResNets
24:10 Diving deeper: stage ratio
27:20 Diving deeper: misc (inverted bottleneck, depthwise conv...)
34:45 Results (classification, object detection, segmentation)
37:35 RIP DanNet
38:40 Summary and outro
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#convnext #visiontransformers #computervision