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Facebook AI & UC Berkeley’s ConvNeXts Compete Favourably With SOTA Hierarchical ViTs on CV Benchmarks
Jan. 13, 2022, 3:21 p.m. | Synced
Synced syncedreview.com
A team from Facebook AI Research and UC Berkeley proposes ConvNeXts, a pure ConvNet model that achieves performance comparable with state-of-the-art hierarchical vision transformers on computer vision benchmarks while retaining the simplicity and efficiency of standard ConvNets.
The post Facebook AI & UC Berkeley’s ConvNeXts Compete Favourably With SOTA Hierarchical ViTs on CV Benchmarks first appeared on Synced.
ai artificial intelligence benchmarks computer vision & graphics convolution neural network cv facebook machine learning machine learning & data science ml research sota technology uc berkeley vision-transformer
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