  Deep learning has shown a tremendous growth in hashing techniques for image
retrieval. Recently, Transformer has emerged as a new architecture by utilizing
self-attention without convolution. Transformer is also extended to Vision
Transformer (ViT) for the visual recognition with a promising performance on
ImageNet. In this paper, we propose a Vision Transformer based Hashing (VTS)
for image retrieval. We utilize the pre-trained ViT on ImageNet as the backbone
network and add the hashing head. The proposed VTS model is fine tuned for
hashing under six different image retrieval frameworks, including Deep
Supervised Hashing (DSH), HashNet, GreedyHash, Improved Deep Hashing Network
(IDHN), Deep Polarized Network (DPN) and Central Similarity Quantization (CSQ)
with their objective functions. We perform the extensive experiments on
CIFAR10, ImageNet, NUS-Wide, and COCO datasets. The proposed VTS based image
retrieval outperforms the recent state-of-the-art hashing techniques with a
great margin. We also find the proposed VTS model as the backbone network is
better than the existing networks, such as AlexNet and ResNet. The code is
released at \url{https://github.com/shivram1987/VisionTransformerHashing}.
