ShiftViT is a variation of the Vision Transformer (ViT) where the attention operation has been replaced with a shifting operation.
ShiftViT model was proposed as part of the paper When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism.
Vision Transformers have lately become very popular for computer vision problems and a lot researchers attribute their success to the attention layers. The authors of the ShiftViT paper have tried to show via the ShiftViT model that even without the attention operation, ViTs can reach SoTA results.
Each stage in the ShiftViT architecture comprises of a Shift Block as shown in Fig 2.
ShiftViT block
Figure 2: From the Model to a Shift Block.
The Shift Block as shown in Fig. 2, comprises of the following:
Shift Operation
Linear Normalization
MLP Layer: stack of densely-connected layers
How shift operation works:
Split the channels
Select each of the first four spilts and shift and pad them in the respective directions.
After shifting and padding, we concatenate the channel back.
Manim rendered animation for shift operation
Figure 3: The TensorFlow style shifting
Intended uses
This ShiftViT model is trained to be used for image classification task.
However, the ShiftViT architecture can be used for a variety of visual recognition tasks.
The authors of the ShiftViT paper tested the model on the following tasks:
Image Classification on ImageNet-1k dataset
Object Detection on COCO dataset
Semantic Segmentation on ADE20k dataset
Training and evaluation data
The dataset used for training the model is CIFAR-10.
The CIFAR-10 dataset is a popular dataset used for image classification. It contains images belonging to the following 10 classes:
Classes
airplane
automobile
bird
cat
deer
dog
frog
horse
ship
truck
No. of samples used for training and evaluation are:
Training samples: 40000
Validation samples: 10000
Testing samples: 10000
Training procedure
Data Preparation:
Data Augmentation: The augmentation steps used for data preparation include: rescaling, resizing, cropping and horizontal flipping.
Building the ShiftViT Model:
The steps for constructing the ShiftViT model have been covered extensively in this Keras example
Model Training:
The model is then trained using the following config:
Training Config
Value
Optimizer
Adam
Loss Function
sparse_categorical_crossentropy
Metric
Accuracy
Epochs
5
Model Testing:
The model is tested on the test data post training achieving an accuracy of ~90%.
Training hyperparameters
The following hyperparameters were used during training:
