THU-MIG: YOLOv12

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.

YOLO26 is a real-time object detection model developed by Ultralytics, released in October 2025. It introduces a native end-to-end, NMS-free architecture that eliminates the Non-Maximum Suppression post-processing step, reducing CPU latency by up to 43% for the Nano variant compared to NMS-dependent versions. The model incorporates the MuSGD optimizer and ProgLoss with STAL for improved training stability and small-object detection, and removes Distribution Focal Loss to ensure maximum compatibility with ONNX and TensorRT export targets.YOLO26 supports object detection, instance segmentation, pose estimation, and oriented bounding box detection within a unified framework, with model sizes available from Nano to Extra Large. Its NMS-free design makes it particularly well suited for deployment scenarios where post-processing overhead is a bottleneck, such as embedded systems and real-time edge inference pipelines.

YOLOv10 is a real-time end-to-end object detection model developed by THU-MIG at Tsinghua University, released in May 2024 under the AGPL-3.0 license. It introduces consistent dual assignments during training — using both one-to-many and one-to-one label assignment strategies — to eliminate the need for non-maximum suppression at inference time while maintaining competitive accuracy. This end-to-end design reduces inference latency compared to NMS-dependent detectors at similar accuracy levels.YOLOv10-B achieves 52.7% AP on COCO with 46% lower latency than YOLOv9-C at comparable performance. The model is available in six sizes from Nano to Extra Large, built on the Ultralytics framework, and exportable to ONNX, TensorRT, and CoreML. YOLOv10 is suited for latency-sensitive deployment scenarios where post-processing overhead is a constraint.

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.

D-FINE is a real-time object detection model introduced in October 2024 by researchers at the University of Science and Technology of China. It builds on the DETR family of transformer-based detectors by reformulating bounding box regression as a Fine-grained Distribution Refinement task. Rather than predicting box coordinates directly, D-FINE iteratively refines probability distributions over coordinate offsets across decoder layers, which provides finer localization granularity without adding inference cost. The architecture also replaces the encoder's CSP blocks with GELAN modules and inserts a Target Gating Layer after the decoder's cross-attention to reduce representational entanglement across queries. A second contribution, Global Optimal Localization Self-Distillation, transfers localization knowledge from refined deeper-layer predictions back to earlier decoder layers through internal self-distillation.D-FINE is released in five model sizes (Nano, Small, Medium, Large, and X), with D-FINE-L achieving 54.0% AP on the Microsoft COCO benchmark at 124 FPS on an NVIDIA T4 GPU, and D-FINE-X reaching 55.8% AP at 78 FPS. Pretraining on the Objects365 dataset further improves accuracy to 57.1% AP for the L variant and 59.3% AP for the X variant. The paper was accepted at ICLR 2025 as a Spotlight. Code and pretrained weights are released under the Apache 2.0 license, making the model suitable for commercial use.

DEIM is a training framework for DETR-based object detection models released in December 2024 by researchers at Intellindust AI Lab, City University of Hong Kong, Great Bay University, and Hefei Normal University. It enhances existing real-time DETR architectures by improving the matcher used during training, enabling faster convergence and higher accuracy without modifying the inference architecture or adding computational overhead at deployment time. DEIM introduces two core techniques: Dense One-to-One (O2O) matching, which increases the number of positive matches per target, and Matchability-Aware Loss (MAL), which down-weights low-quality matches generated by the dense strategy. The paper was accepted at CVPR 2025.When integrated with RT-DETR and D-FINE, DEIM consistently improves performance while reducing training time by up to 50%. Applied to RT-DETRv2, it achieves 53.2% AP with a single day of training on an NVIDIA 4090 GPU. DEIM-enhanced models including DEIM-D-FINE-L and DEIM-D-FINE-X achieve 54.7% and 56.5% AP at 124 and 78 FPS respectively on an NVIDIA T4 GPU. DEIM is released under the Apache 2.0 license. A successor, DEIMv2, was released in September 2025, adding DINOv3-based backbones and introducing ultra-lightweight variants (Pico, Femto, and Atto) for edge deployment.