๐งจEfficientFormers: 1.6ms inference ๐งจ
๐Transformers fast as MobileNet? Snap shows that on #iphone!
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Low latency on mobile, high performance!
โ Revisiting the design of ViT through latency
โ New dimension-consistent design paradigm
โ EfficientFormers: a new ViT for mobile!
More: https://bit.ly/3MdgW15
๐Transformers fast as MobileNet? Snap shows that on #iphone!
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ Low latency on mobile, high performance!
โ Revisiting the design of ViT through latency
โ New dimension-consistent design paradigm
โ EfficientFormers: a new ViT for mobile!
More: https://bit.ly/3MdgW15
๐ฅ16๐1๐คฏ1
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๐ฒ EdgeSAM: Mobile 40x SAM ๐ฒ
๐A novel hyper-optimized version of SAM for mobile devices such as #Iphone. Pure CNNs backbone (better suitable for ANE), up to 40x faster. Code available ๐
๐Review https://t.ly/m_vLH
๐Paper https://lnkd.in/gHZVZN2x
๐Project https://lnkd.in/gK8qEK8p
๐Repo https://lnkd.in/gj6YAGNv
๐Hugging Face https://lnkd.in/gUUHJvxz
๐A novel hyper-optimized version of SAM for mobile devices such as #Iphone. Pure CNNs backbone (better suitable for ANE), up to 40x faster. Code available ๐
๐Review https://t.ly/m_vLH
๐Paper https://lnkd.in/gHZVZN2x
๐Project https://lnkd.in/gK8qEK8p
๐Repo https://lnkd.in/gj6YAGNv
๐Hugging Face https://lnkd.in/gUUHJvxz
๐ฅ20โก2โค2๐คฉ1