BUTom-ST21: Spatial-temporal tomato data enabling detection, segmentation, tracking, and video-instance segmentation using strong and weak labels

We employed BUTom21 still image dataset and generated spatial-temporal tomato dataset using a neural-radiance field based approach. In total we have 217 video sequences (123 train/ 72 valid/ 22 eval) can be used for image-based/video-based detection and segmentation, and multi object tracking.

The train and validation sets are weakly labeled(pseudo-labeled), and evaluation set is hand-labeled ground-truth. BUTom-ST21 has 7749, and 4536 pseudo-labeled images across the training and validation set, and 1386 images hand-labeled in the evaluation set. Additionally it has camera poses per sequence, enabling the use of 3D reconstruction purposes in horticultural field.