The top histogram shows the distribution for the whole data set, and the bottom histograms show the distributions for the low, medium, and high levels of complexity. In a range of 18 to 661 cells, the majority of images had at most 250 cells. Only eleven images had fewer than 50 cells, and nineteen images had more than 250.

Although it is normally distributed, we can verify the significant variance of cell morphology available in the data set.

The cells’ ratio, which can influence the aspect ratio definition in Faster R-CNN, varied between 0.3 and 3.3.

The magenta shape corresponds to a 1:2 aspect ratio, the blue corresponds to a 2:1 aspect ratio, and the green corresponds to a square. The red dot is the anchor.

Faster algorithms such as SSD cannot deal with the complexity of the problem. Faster R-CNN emphasizes accuracy over speed and can achieve over six times better performance than SSD with the same feature extractor. YOLO v5, the last version of YOLO, outperforms SSD and RFCN, but Faster R-CNN still has an advantage of 0.1 mAP at 4,000 steps.

When dealing with very small cells with a small number of pixels, it is important to increase the resolution of the image to improve the richness of features. In this way, the detector can correctly identify and classify the cells.

Adam, which is known for achieving a faster convergence than the remaining optimizers, cannot achieve a satisfying performance if the learning rate decay is not well adjusted.

After a set of experiments where we test the learning rate with different orders of magnitude, the range of values between 0.0001 and 0.0005 leads to an improvement in the mAP. In this range, the value of 0.0002 results in faster convergence.