Major changes:
- YOLOv2 is replaced with cut-down version of YOLOv3. Additional layers in the second branch are removed to fit K210 memory limit. Testing will continue to see how large impact does it have on accuracy. Currently it seems that for small number of classes YOLOv3 performs significantly better than YOLOv2 (especially with not square input size, e.g. 320x240), but for PASCAL VOC 20 classes model often mixes up minor classes (cat vs. dog, sheep vs. horse, etc.). YOLOv2 is still available in legacy-yolov2 branch.
Minor changes:
- Evaluation script added. For classification task confusion matrix, precision, recall and f1-score are calculated. For detection, mAP, precision, recall and f1-score are calculated. For segmentation task, mean IoU and class-wise IoU are calculated. Evaluation script creates report.txt file in project folder, where config and evaluation results are saved.
Note YOLOv3 currently only works for MobileNet feature extractors. In the next version, my plan is to completely re-work the backend system, making it more concise and tailored for embedded systems.