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The rapidly evolving industry demands high accuracy of the models without theneed for time-consuming and computationally expensive experiments required forfine-tuning. Moreover, a model and training pipeline, which was once carefullyoptimized for a specific dataset, rarely generalizes well to training on adifferent dataset. This makes it unrealistic to have carefully fine-tunedmodels for each use case. To solve this, we propose an alternative approachthat also forms a backbone of Intel Geti platform: a dataset-agnostic templatefor object detection trainings, consisting of carefully chosen and pre-trainedmodels together with a robust training pipeline for further training. Oursolution works out-of-the-box and provides a strong baseline on a wide range ofdatasets. It can be used on its own or as a starting point for furtherfine-tuning for specific use cases when needed. We obtained dataset-agnostictemplates by performing parallel training on a corpus of datasets andoptimizing the choice of architectures and training tricks with respect to theaverage results on the whole corpora. We examined a number of architectures,taking into account the performance-accuracy trade-off. Consequently, wepropose 3 finalists, VFNet, ATSS, and SSD, that can be deployed on CPU usingthe OpenVINO toolkit. The source code is available as a part of the OpenVINOTraining Extensions (https://github.com/openvinotoolkit/training_extensions}

How to Train an Accurate and Efficient Object Detection Model on Any Dataset