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Efficient360: Efficient Vision Transformer

The efficient 360 framework is a collection of transformer models in various dimensions.

Paper

Various Dimensions of Efficient360

Efficient 360

Vision Transformer Models and their comparisons.

model

Inference on Deit-based transformer Model and its Grad-CAM Explanation

Deit Model Inference

Architectural performance of Various Transformer Models

Model Performance

Architectural performance of Various SOTA Transformer Models

All Model Performance

State of the Art results of various vision transformer models on ImageNet-1K dataset with Image size 224 x 224.

SOTA224

State of the Art results of various vision transformer models on ImageNet-1K dataset with different Image sizes.

SOTA

State of the Art results of various vision transformer models on ImageNet-22K dataset with different Image sizes.

SOTA1

State of the Art results of various Spectral Vision Transformer models on ImageNet-1K dataset with different Image sizes.

SOTA2

Transfer Learning results of various datasets like CIFAR10, CIFAR100, Pet, Flower, and Cars datasets, The models are pre-trained on ImageNet-1K and ImageNet-22K datasets.

SOTA3

Long Range Arena (LRA) Benchmark Datasets and its corresponding tasks.

SOTA4

Citation:


@article{efficient3602023,
  title={Efficient360: Efficient Vision Transformer},
  author={Patro, Badri N. and Agneeswaran Vijay S. },
  journal={arXiv preprint arXiv:2302.08374},
  year={2023}
}

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