Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification

These are companion data and models of manuscript "Self-Supervised Maize Kernel Classification and Segmentation for Embryo Identification" that was submitted to Frontiers in Plant Science.

The data is organized into three main folders: 'class_full_imgs', 'seg_full_imgs', and 'unlabeled'. 

The 'class_full_imgs' folder contains labeled data used to train the classification model, which is divided into train, validation, and test subfolders. Each of these subfolders contains 'oriented' and 'non-oriented' images. 

The 'seg_full_imgs' folder contains labeled data used to train the segmentation model. The 'InputImages' subfolder contains raw images, and the 'OutputImages' subfolder contains the segmented images. 

The 'unlabeled' folder contains images without any labels. These images were used for self-supervised pretraining of classification and segmentation models.

The trained models can be found in Trained_models.zip. There are four zip files:
- "simclr_pretrained_bb.zip" contains the selected pretrained backbone trained via SimCLR.
- "nnclr_pretrained_bb.zip" contains the selected pretrained backbone trained via NNCLR.
- "finetuned_classification" contains classification models which have undergone end-to-end finetuning, split into supervised and self-supervision-pretrained models.
- "segmentation" contains image segmentation models, where the names refer to the pretraining method.