# Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools
## 5 Discussion  
The achieved overall score breaks the expected values in a specific OCR scope, resulting in a high textual data detection accuracy with mAP = 0.65. As it is visible on the charts of Figures 3 and 4, during first experiment with DETR (mAP = 0.174) and second experiment with DETR (mAP = 0.39), there is room for improvement by increasing the number of epochs since the model did not yet produce overfitting. During the third experiment with DETR (mAP = 0.5) depicted in Figure 6, we can see that the model reaches its best after 400 epochs, and there is only a tiny increment after that point. The model is not overfitting, but it cannot improve its performance. We observed very similar results in the fourth experiment with DETR (mAP = 0.65) exhibited in Figure 5, but with a limited number of categories. However, a considerably higher mAP score has been achieved almost in the same number of epochs. We checked the results for our Textual Object Detection at this point of research. The system had not reached the 0.90 value with mAP, just 0.65 (see Figure 9); nonetheless, the focused objects were identified accurately. In Phase 1 of this research, we applied the DETR DL-based model structure. It used only 248 manually collected and annotated training examples and 55 validation examples to fit the technical fminimum viable model for practical statistical learning. To achieve the theoretical maximum with mAP exceeding 0.90 (e.g., in the case of context-sensitivelanguage detection), the model requires a minimum set of 1500 training with 200 validation data, 1700 instances combined, according to our calculations based on research experiments. The original goal of the research was to prove that detecting textual elements with Visual Transformers and fully customizable ML models is possible with suitable accuracy by applying screenshot images. Figure 3 shows that there is room for refinement by enriching the number of epochs because the model is not overfitted yet. Overfitting can be determined from loss functions: we can reach the starting point of overfit when the training loss decreases, but the validation loss starts to increase. Figure 4 shows the 2 nd experiment results, and it is observable that we can still boost the epoch number. Figure 6 shows the 3 rd experiment results. The model is reaching its best after 400 epochs, and there is only a small increment after that point. We have to change the type of experiment if we want to improve the model. The model is not overfitted yet, but it cannot improve anymore via learning. At this point, we obtained \(\mathrm{mAP}=0.5\). Figure 5 shows the 4 th experiment results with 1000 epochs with a clustered approach, and this experiment brings the real breakthrough; a much better score (\(\mathrm{mAP}=0.65\)) was reached in almost the same number of epochs (see Figure 5). Figure 9 shows the OCR output connected to the 4 th experiment. The feasibility checking and validation experiment emphasize that the models recognize text boxes and categories with high accuracy, specific to the requirements of the textual topic approach. Each experiment group gave the same level of performance (mAP between 0.4 and 0.65) irrespective of the actual model types. The main bottleneck for further improvement is the limited data quantity and quality and the coverage of all use cases. For this reason, we improved the annotation protocols, annotation review process, and image acquisition and selection guidelines to start the second research phase. **YOLO** reached its best (\(\mathrm{mAP}=0.413\)) with YOLOv4 Tiny (see Table 3). YOLO's disadvantage was the slow training process. **Detectron**'s best score of \(\mathrm{mAP}=0.5\) is coming from Retinanet R101 FPN 3x (see Table 1). Detectron2's disadvantage is the complex framework. Finally, **DETR**'s best of \(\mathrm{mAP}=0.65\) came from DETR RESNET50 (see Figure 5). Disadvantages of this model are the slow training, separated detection, and classification model.