## 3 Benchmark  
**The training setting:** BBRefinement was trained using an RTX2080 graphics card with 11 GB VRAM. The resolution of the models corresponds to the default setting of the EfficientNet [16] version, namely, the side size of 224, 240, 260, 300, and 380 px for version B0-B4. The batch size was 7-40 according to the version and memory of the graphics card. From the COCO dataset [1], we took train2017 as a training set and val2017 as a testing set. The loss function is the linear IoU, the optimizer is AdaDelta [20] with default learning rate, i.e., \(\alpha=1.0\), and we used the functionality of decreasing the learning rate by a factor 0.5 with patience equal to three. We also experimented with the cyclic LR [21], which converged faster but did not reach the best possible loss. During one time period, all training images were processed, and a single random box was taken from each one of them. Each such box was augmented by random padding, linear/non-linear HSV distortion, CLAHE [22], and flipping. The information about the box center was augmented by distorting the coordinates. The illustration of the augmented box is shown in Figure 6. Models were trained until the loss did not stop decreasing, which took approx 70-90 time periods. For illustration, the heaviest backbone, EfficientNet B4, was trained for nine days. We selected the most frequently used SOTA networks for the benchmark; see the complete list in Table 1. The models derived from Faster R-CNN [3], Mask R-CNN [23], RetinaNet [24], and Cascade R-CNN [9] are from the Detectron2 framework ([[https://github.com/facebookresearch/detectron2](https://github.com/facebookresearch/detectron2)]([https://github.com/facebookresearch/detectron2](https://github.com/facebookresearch/detectron2)) accessed on 17 November 2020). For DETR [14], we used the official minimalistic implementation ([[https://github.com/facebookresearch/detr](https://github.com/facebookresearch/detr)]([https://github.com/facebookresearch/detr](https://github.com/facebookresearch/detr)) accessed on 11 November 2020). Models derived from YOLOv3 [17] and SSD [11] are taken from the MMDetection framework ([[https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection)]([https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection)) accessed on 15 December 2021). For these networks, we used their reference models trained on COCO train2017 and measured the impact of the BBRefinement. **The detailed results** are presented in Table 1. We want to emphasize that BBRefinement improves the mAP of all but Cascade R-CNN models, considering the standard (IoU = 0.50:0.95) setting, while the heavier backbone of BBRefinement usually provides a stronger boost. This is not valid for EfficientNetB4, where we suppose the performance drop is caused by training with a batch size that is too small. There is a hypothesis that training BBRefinement on a more powerful graphics card with a larger VRAM can increase performance; for verification, see Table 2 in [25] or the study in [26]. Furthermore, it roughly holds that the worse the baseline model, the larger the increase in mAP. Considering the objects' size according to the COCO tools (small, medium, or big), the larger the object, the larger the boost that is obtained. There is a hypothesis that strong upscaling of small objects leads to distortion and, therefore, to decreased performance. Thus, searching for a customized backbone, e.g., with switchable atrous convolutions [27], is a reasonable direction for future development. It also shows that datasets consisting of small objects only are a contraindication for BBRefinement. On the other hand, having the dataset with mostly medium and large objects, the involving of BBRefinement will lead to significant improvement. Figure 6: Augmentations of a box. The green box represents the GT label. The original crop is randomly padded.