Abstract
Transformer has been proposed to augment the attention mechanism in neural networks without using recurrence and convolutions. Starting with machine translation, it graduated to vision transformer. Among the vision transformers, we explore the DEtection TRansformer (DETR) model proposed in the End-to-end Object Detection with Transformers paper by the team at Facebook AI. The authors have demonstrated interesting object detection results from the DETR model. That triggered the curiosity to use the model for detection of custom objects. Here, we are presenting the way to fine-tune the pre-trained DETR model over custom dataset. The fine-tuning results demonstrate significant improvement with respect to number of training epochs, both visibly as well as statistically.
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Acknowledgements
We thank DIPR, DRDO for providing the R&D environment to carry out the research work. We also thank IIIT Allahabad for providing the opportunity to carry out the PhD course under the Working Professional Scheme.
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Kumar, A., Singh, S.K., Dubey, S.R. (2023). Target Detection Using Transformer: A Study Using DETR. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_59
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