## 5 Ablation Study  
**Comparing with a naive refinement without mixture data:** We trained BBRefinement with EfficientNetB1 with the same setting as was used in Section 3, but without mixture data. This means that only visual information represented by image crops was available during training and inference. During the inference, we coupled it with 'Faster R-CNN, ResNet-50 C4 1x'. It achieved mAP performance on all/small/medium/large areas of 36.6/19.3/40.8/50.4, which is better than the baseline performance of 35.7/19.2/40.9/48.7 but worse than the full couple with the mixture data, which yielded 37.4/19.3/42.6/52.8. This finding confirms the meaningfulness of the proposed scheme.  
**Influence of the accuracy of center and class:** The performance of BBRefinement is affected by the accuracy of the object detector used. Therefore, we realized an experiment in which GT data were distorted and fed into BBRefinement, and measured IoU between the refined and GT boxes. As we demonstrate in Figure 10, we distorted the position of the center and the correct class separately. The distortion of center **c** was realized as \(\textbf{c}=(c_{x}+d_{x},c_{y}+d_{y})\), where \(d_{x},d_{y}\sim\mathcal{U}(-d,d)\), and by \(d\) we mean the maximum distortion.  
Figure 8: The image illustrated crops with green ground truth and yellow refined inpainted labels. Here, BBRefinement creates labels with significantly higher precision than the ground truth. Best seen zoomed-in.  
Figure 9: The image illustrated crops with green ground truth and yellow refined inpainted labels. The crops were selected uniformly according to their index to show general cases. Here, BBRefinement provides slightly higher precision than ground truth. Best seen zoomed-in.  
For class distortion, we replaced \(n\%\) of correct classes with random incorrect classes. BBRefinement is robust for incorrect classes, but sensitive to center position distortion.  
**Influence of crop size:** We selected BBRefinement with the EfficientNetB2 backbone and trained it for various crop sizes to reveal the impact. To converge faster, we weakened the setting compared to the 'full experiment', namely, 3000 steps per time period, batch size 8, and patience 1 in reducing the learning rate. During the inference, it was coupled with 'Faster R-CNN, ResNet-50 C4 1x'. According to the graph in Figure 10, the model was stable and the crop size had a minor impact for all sizes except 600, where there was a decrease in performance. For the extreme case of crop size 600, almost all objects, including the large ones, were upsampled, which distorted them.  
**Speed of inference:** BBRefinement's inference time consists of two parts, the preparation of the crops and the inference itself. While the first part depends on the CPU, the second part relies only on the GPU power. We measured the processing time of BBRefinement with EfficientNetB1 and EfficientNetB3 backbones. For both cases, the non-optimized preparation of crops on CPU cost 32 ms per whole image, where the image could include multiple crops. The time on GPU was 23 ms for B1 and 44 ms for B3. BBRefinement predicted all boxes of a single image in one batch, which kept the time small. The aggregation of both CPU+GPU times means that BBRefinement ran 18 FPS for the B1 backbone and 13 FPS for the B3 backbone. The processing speed could be further increased by parallelizing the crops' preparation and optimizing the model, e.g., by fusing a batch normalization layer with a convolution layer into a single layer.