Megvii: YOLOX

YOLOv8 is an object detection and multi-task vision model developed by Ultralytics, released in January 2023 under the AGPL-3.0 license. It succeeds YOLOv5 and introduces an anchor-free detection head, a new C2f module for improved gradient flow, and a decoupled head that separates classification and regression tasks. These changes improve both accuracy and training efficiency compared to earlier Ultralytics models.YOLOv8 supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a unified codebase. It is available in five sizes from Nano to Extra Large and exports to ONNX, TensorRT, CoreML, and other formats. YOLOv8 is one of the most widely adopted detection models in production and is directly supported by Roboflow Inference for custom model training and deployment.

YOLO11 is an object detection and multi-task vision model developed by Ultralytics, released in September 2024 under the AGPL-3.0 license. It is the latest generation in the Ultralytics YOLO series and supports object detection, instance segmentation, image classification, pose estimation, and oriented bounding box detection within a single unified framework. YOLO11 introduces architectural refinements that improve accuracy while reducing parameter count compared to YOLOv8 at equivalent model sizes.YOLO11 is available in five model sizes from Nano to Extra Large and is deployable through the Ultralytics Python package, Roboflow Inference, and export formats including ONNX, TensorRT, and CoreML. It supports fine-tuning on custom datasets through the standard Ultralytics training API.

YOLOv9 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in February 2024 under the GPL-3.0 license. It introduces Programmable Gradient Information (PGI), a mechanism that preserves complete input information through auxiliary reversible branches during training to address information loss in deep network layers. It also introduces the Generalized Efficient Layer Aggregation Network (GELAN), which achieves better parameter utilization compared to prior CSP-based designs.YOLOv9-C achieves 53.0% AP on COCO with 42% fewer parameters and 21% less computation than YOLOv8-C at comparable accuracy. YOLOv9-E achieves 55.6% AP. The model is deployable through Roboflow Inference and supports fine-tuning via the standard training pipeline in the official repository.

RTMDet is a real-time object detection model developed by OpenMMLab, released in December 2022 under the GPL-3.0 license. It adopts a single-stage detection architecture with large-kernel depthwise convolution in both the backbone and neck, enabling it to capture long-range spatial dependencies without the computational cost of full self-attention. The model family spans from RTMDet-tiny to RTMDet-x, covering a wide range of speed-accuracy operating points.RTMDet-x achieves 52.6% AP on COCO at 114 FPS on an NVIDIA 3090 GPU. The architecture supports instance segmentation and rotated object detection variants. RTMDet is included in the OpenMMLab ecosystem and is well suited for applications requiring fast, accurate detection with flexible model sizing.

YOLOv7 is a real-time object detection model developed by Chien-Yao Wang and Hong-Yuan Mark Liao at Academia Sinica, released in July 2022 under the GPL-3.0 license. It introduces Extended Efficient Layer Aggregation Networks (E-ELAN) for improved gradient flow in the backbone, and trainable bag-of-freebies techniques including coarse-to-fine lead guided label assignment and auxiliary heads that improve accuracy without adding inference cost.YOLOv7 achieves 56.8% AP on COCO at 30 FPS on a V100 GPU at the time of release, establishing a strong accuracy-speed tradeoff among real-time detectors. It supports detection, instance segmentation, and pose estimation variants. YOLOv7 is deployable through Roboflow Inference and the standard training pipeline in the official repository.