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