--- annotations_creators: - expert-annotated language: - en license: other multilinguality: monolingual dataset_name: Gilt Posture Dataset task_categories: - object-detection - image-classification task_ids: - multi-class-image-classification tags: - animal-behavior - pigs - rgb-d - depth-sensing - yolo - posture --- # Gilt Posture Recognition Dataset - Each RGB image has a matching depth image (same filename, `.png` extension). - YOLO-format label files correspond to each image. ## 🐷 Annotated Postures Five postures are labeled using YOLO bounding boxes: | Class Name | Class ID | |------------------|----------| | feeding | 0 | | lateral_lying | 1 | | sitting | 2 | | standing | 3 | | sternal_lying | 4 | ## 📊 Class Distribution Below is a histogram showing the distribution of posture classes across the dataset: ![Class Histogram](assets/class_histogram.png) ## Dataset Description The total dataset is split randomly into training, validation, and testing sets (0.75:0.15:0.1). The filename of each image and corresponding labels are assigned with date and time of image captured prefixed by pen and camera identity (p1c1_20250108_080409.png == image of pen1 camera1 captured on January 08, 2025 at 08:04:09 o'clock) - The Color folder contains the color images and corresponding labels. - Depth folder contains the height information of scene from the floor in mm unit and saved as uint16 format. - RGBD folder contains the combined pairs of color and depth images. The normalized height information is added as 4th channel (RGBA). - Each folder contains a labels folder for the corresponding labeling information ## 🧠 Use Cases - Animal behavior monitoring - Multimodal object detection (RGB + Depth) - Precision livestock farming ## License The author has granted permission to download, use and redistribute this dataset only for research purposes. ## Citation Please cite as Bhujel A. et al. (2025). A Computer Vision dataset for Gilts' daily activity monitoring and tracking. ## Contact For questions or collaborations, feel free to reach out at bhujelan@msu.edu