- YOLOv3: The third iteration of the YOLO model family, originally by Joseph Redmon, known for its efficient real-time object detection capabilities.
- YOLOv4: A darknet-native update to YOLOv3, released by Alexey Bochkovskiy in 2020.
- YOLOv5: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
- YOLOv6: Released by Meituan in 2022, and in use in many of the company's autonomous delivery robots.
- YOLOv7: Updated YOLO models released in 2022 by the authors of YOLOv4.
- YOLOv8 NEW 🚀: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
- YOLOv9: An experimental model trained on the Ultralytics YOLOv5 codebase implementing Programmable Gradient Information (PGI).
- YOLOv10: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
- Segment Anything Model (SAM): Meta's Segment Anything Model (SAM).
- Mobile Segment Anything Model (MobileSAM): MobileSAM for mobile applications, by Kyung Hee University.
- Fast Segment Anything Model (FastSAM): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
- YOLO-NAS: YOLO Neural Architecture Search (NAS) Models.
- Realtime Detection Transformers (RT-DETR): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
- YOLO-World: Real-time Open Vocabulary Object Detection models from Tencent AI Lab.
