HyperAI초신경

홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

한국어

HyperAI초신경

Efficient Vits On Imagenet 1K With Lv Vit S

평가 지표

GFLOPs

Top 1 Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	GFLOPs	Top 1 Accuracy	Paper Title	Repository
MCTF ($r=16$)	3.6	82.3	Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers
MCTF ($r=8$)	4.9	83.5	Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers
DynamicViT (70%)	4.6	83.0	DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
Base (LV-ViT-S)	6.6	83.3	All Tokens Matter: Token Labeling for Training Better Vision Transformers
eTPS	3.8	82.5	Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers
SPViT	4.3	83.1	SPViT: Enabling Faster Vision Transformers via Soft Token Pruning
DynamicViT (80%)	5.1	83.2	DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification
EViT (50%)	3.9	82.5	Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
DPS-LV-ViT-S	4.5	82.9	Patch Slimming for Efficient Vision Transformers	-
PS-LV-ViT-S	4.7	82.4	Patch Slimming for Efficient Vision Transformers	-
DiffRate	3.9	82.6	DiffRate : Differentiable Compression Rate for Efficient Vision Transformers
MCTF ($r=12$)	4.2	83.4	Multi-criteria Token Fusion with One-step-ahead Attention for Efficient Vision Transformers
AS-LV-S (60%)	3.9	82.6	Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention	-
EViT (70%)	4.7	83.0	Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
AS-LV-S (70%)	4.6	83.1	Adaptive Sparse ViT: Towards Learnable Adaptive Token Pruning by Fully Exploiting Self-Attention	-
dTPS	3.8	82.6	Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers
BAT	4.7	83.1	Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision Transformers
PPT	4.6	83.1	PPT: Token Pruning and Pooling for Efficient Vision Transformers
DynamicViT (90%)	5.8	83.3	DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification

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