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Slimmable ConvNeXt enables adaptive vision model deployment

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Slimmable ConvNeXt, a novel approach to creating adaptable vision models. This method trains a single set of weights that can dynamically adjust its capacity for efficient deployment across various devices and fluctuating computational resources. The Slimmable ConvNeXt-T model achieves 80.8% accuracy on ImageNet-1k with 4.5 GMACs, outperforming existing scalable methods like HydraViT and MatFormer-S. AI

IMPACT Enables more efficient deployment of vision models across diverse hardware, reducing the need for multiple model versions.

RANK_REASON The cluster contains an arXiv paper detailing a new model architecture and its performance on benchmarks.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Janek Haberer, Jon Eike Wilhelm, Olaf Landsiedel ·

    Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment

    arXiv:2605.22677v1 Announce Type: new Abstract: Deploying vision models across devices with varying resource constraints, or even on a single device where available compute fluctuates due to battery state, thermal throttling, or latency deadlines, typically requires training and …

  2. arXiv cs.CV TIER_1 English(EN) · Olaf Landsiedel ·

    Slimmable ConvNeXt: Width-Adaptive Inference for Efficient Multi-Device Deployment

    Deploying vision models across devices with varying resource constraints, or even on a single device where available compute fluctuates due to battery state, thermal throttling, or latency deadlines, typically requires training and maintaining separate models. Width-adaptive infe…

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