HotPig: a behavioural dataset of pigs under heat stress

Data were collected on 24 pigs that were video-monitored day and night under two contrasted conditions: thermoneutral (TN, 22°C) and Heat Stress (32°C). All pigs were housed individually and had free access to an automatic electronic feeder delivering pellets four times a day, and to water. After acquisition, videos were processed using YOLOv11, a real-time object detection algorithm object detector that uses a convolutional neural network (CNN), to extract the following behavioural traits: drinking, willingness to eat, lying down, standing up, moving around, curiosity towards the littermate housed in the neighbouring pen, and contact between the two animals (cuddling). A minute frequency basis was applied (each minute correspond to 150 frames processed) for a continuous period of 16 days, spanning the two different thermal conditions (9 days on TN, 6 days on HS, 1 day back to TN). The algorithm was first trained thanks to manual video analysis labelling at the individual scale. Consistency with the automatic electronic feeder’s data (also provided) was thoroughly checked. The dataset allows quantitative criterion to be analysed to decipher inter-individual differences in animal behaviour and their dynamic adaptation to heat stress. This dataset can be used to train any machine learning methods for behaviour prediction from videos in conventional growing pigs.