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