Efficient Vits On Imagenet 1K With Lv Vit S

Metrics

GFLOPs

Top 1 Accuracy

Results

Performance results of various models on this benchmark

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

0 of 19 row(s) selected.

Efficient Vits On Imagenet 1K With Lv Vit S

Metrics

GFLOPs

Top 1 Accuracy

Results

Performance results of various models on this benchmark

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

0 of 19 row(s) selected.

Efficient Vits On Imagenet 1K With Lv Vit S | SOTA | HyperAI