8-Calves Image dataset
This addresses a problem for precision farming by providing a challenging dataset to improve agricultural vision models, though it is incremental as it builds on existing methods like YOLOv8 and ByteTrack.
The researchers tackled the lack of realistic datasets for automated livestock monitoring by introducing the 8-Calves dataset, a benchmark for multi-animal detection, tracking, and identification, showing that while object detectors achieve high performance on lenient metrics (e.g., mAP50 up to 98.9%), they struggle with stricter localization (mAP50:95 down to 56.5%) and identity preservation (IDF1 around 0.27).
Automated livestock monitoring is crucial for precision farming, but robust computer vision models are hindered by a lack of datasets reflecting real-world group challenges. We introduce the 8-Calves dataset, a challenging benchmark for multi-animal detection, tracking, and identification. It features a one-hour video of eight Holstein Friesian calves in a barn, with frequent occlusions, motion blur, and diverse poses. A semi-automated pipeline using a fine-tuned YOLOv8 detector and ByteTrack, followed by manual correction, provides over 537,000 bounding boxes with temporal identity labels. We benchmark 28 object detectors, showing near-perfect performance on a lenient IoU threshold (mAP50: 95.2-98.9%) but significant divergence on stricter metrics (mAP50:95: 56.5-66.4%), highlighting fine-grained localization challenges. Our identification benchmark across 23 models reveals a trade-off: scaling model size improves classification accuracy but compromises retrieval. Smaller architectures like ConvNextV2 Nano achieve the best balance (73.35% accuracy, 50.82% Top-1 KNN). Pre-training focused on semantic learning (e.g., BEiT) yielded superior transferability. For tracking, leading methods achieve high detection accuracy (MOTA > 0.92) but struggle with identity preservation (IDF1 $\approx$ 0.27), underscoring a key challenge in occlusion-heavy scenarios. The 8-Calves dataset bridges a gap by providing temporal richness and realistic challenges, serving as a resource for advancing agricultural vision models. The dataset and code are available at https://huggingface.co/datasets/tonyFang04/8-calves.