Usage: To train the neural network, the specified folder for the RGB images is checked first with the os library and the paths of all contained jpg files are stored as strings in a list. This list is then iterated and the first image is loaded via the openCV library. The filename of each RGB image contains a number and an ID as unique identifier. This is used to identify the associated labels. For example, image with Filename "img_038_id2.jpg" has the ID 2. In the associated masks folder, all image files are loaded that also have the 038_id2 in the file name, such as "m5_img_038_id2.jpg". So for each RGB image a list of mask images, also loaded with openCV, is kept in temporary memory. All images then run through the sliding window algorithm and are presented to the network in individual parts one after the other.

The dataset consists of images of C. elegans in Petri Dish that were captured at a frequency of 1 Hz at 3280 × 2464 pixels via a Raspberry Pi based DIY Microscope. Further details of the recording setup and the dataset can be found in the corresponding article. Up on use, please cite the following article https://doi.org/10.3390/bios11080257 such as: Fudickar, S.; Nustede, E.J.; Dreyer, E.; Bornhorst, J. Mask R-CNN Based C. Elegans Detection with a DIY Microscope. Biosensors 2021, 11, 257. https://doi.org/10.3390/bios11080257