Comparing Vision Transformers: A Complete Guide

A detailed comparative analysis of the main transformers for computer vision.

Comparison Table

Transformer	Year	Complexity	Memory	Accuracy	Speed	Use Cases
ViT	2020	O(n²)	Medium	Good	Medium	Classification
DeiT	2020	O(n²)	Low	Very good	Fast	Classification
Swin	2021	O(n)	Medium	Excellent	Fast	Classification, Detection
PVT	2021	O(n)	Low	Good	Very fast	Classification, Segmentation
ViTAE	2021	O(n)	Medium	Excellent	Medium	Classification, Detection
ViTPose	2022	O(n)	Medium	Excellent	Medium	Pose Estimation
ViTPose++	2022	O(n)	Medium	Excellent	Fast	Pose Estimation
DETR	2020	O(n²)	High	Good	Slow	Object Detection
YOLOS	2021	O(n²)	Medium	Good	Medium	Object Detection

Detailed Analysis

1. Vision Transformer (ViT)

Advantages:

• Simple and efficient architecture
• Strong performance on large datasets
• Easy to implement

Disadvantages:

• Quadratic complexity
• Requires a lot of training data
• Limited performance on small datasets

2. DeiT (Data-efficient Image Transformer)

Advantages:

• Efficient training with little data
• Knowledge distillation
• Good performance on small datasets

Disadvantages:

• Requires a teacher model
• Increased training complexity
• Longer training time

3. Swin Transformer

Advantages:

• Linear complexity
• Strong performance on vision tasks
• Efficient hierarchical architecture

Disadvantages:

• More complex architecture
• Requires more memory
• Longer implementation time

4. PVT (Pyramid Vision Transformer)

Advantages:

• Efficient pyramid architecture
• Strong performance on segmentation tasks
• Linear complexity

Disadvantages:

• Limited performance on some tasks
• Requires more parameters
• Variable inference time

5. ViTAE (Vision Transformer with Attention Efficiency)

Advantages:

• Efficient attention
• Strong overall performance
• Modular architecture

Disadvantages:

• Implementation complexity
• Requires more memory
• Longer training time

6. ViTPose/ViTPose++

Advantages:

• Excellent performance on pose estimation
• Multi-scale architecture
• Good handling of spatial relationships

Disadvantages:

• Specialized for pose estimation
• Increased complexity
• Requires more resources

Metrics Comparison

Performance on ImageNet

Model	Top-1 Acc.	Top-5 Acc.	Params (M)	FLOPs (G)
ViT-B	77.9%	93.5%	86	17.6
DeiT-B	81.8%	95.6%	86	17.6
Swin-B	83.5%	96.5%	88	15.4
PVT-L	81.7%	95.7%	61	9.8
ViTAE-B	82.3%	96.1%	86	17.6

Performance on COCO

Model	mAP	AP50	AP75	FPS
DETR	42.0	62.4	44.2	12
YOLOS	42.3	63.0	44.9	15
Swin-T	43.7	66.6	47.7	18
PVT-L	44.1	66.7	48.1	20

Usage Recommendations

1. Image Classification

   • For large datasets: ViT or Swin
   • For small datasets: DeiT
   • For real time: PVT

2. Object Detection

   • For accuracy: Swin
   • For speed: PVT
   • For balance: YOLOS

3. Pose Estimation

   • For accuracy: ViTPose++
   • For real time: ViTPose
   • For balance: Swin + Pose Head

4. Segmentation

   • For accuracy: Swin
   • For speed: PVT
   • For balance: ViTAE

Conclusion

The choice of transformer depends heavily on the application and its specific constraints. Here is a quick guide:

• Accuracy First: Swin or ViTPose++
• Speed First: PVT
• Efficiency First: DeiT
• Flexibility First: ViTAE

Additional Resources

• Papers with Code - Vision Transformers
• GitHub - Awesome Vision Transformers
• PyTorch Documentation
• Vision Transformer Tutorial
• Transformer Zoo