Ultralytics: YOLOv8 Pose Estimation

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.

YOLOv12 is an attention-centric real-time object detection model developed by researchers at Tsinghua University, with the arXiv paper published in February 2025 under the AGPL-3.0 license. It introduces an Area Attention module that partitions feature maps into regions and applies self-attention within each region, reducing the quadratic complexity of full self-attention while capturing long-range dependencies. It also incorporates R-ELAN for improved feature aggregation and scaled residual connections for training stability.YOLOv12-L achieves 54.0% AP on COCO, while the YOLOv12-N variant achieves 40.5% mAP at 1.62ms latency on an NVIDIA T4 GPU. The model is built on the Ultralytics codebase, supporting detection, segmentation, and other standard YOLO tasks at competitive real-time speeds.

MediaPipe is an open-source framework developed by Google for building real-time machine learning pipelines across mobile, web, desktop, and edge platforms. First released in 2019, the framework uses a graph-based architecture where pre-built components called Calculators process streaming data such as images, video, and audio through configurable computation graphs. This design allows developers to compose perception pipelines from reusable building blocks without writing custom glue code between models. The current MediaPipe Tasks API replaces the earlier Solutions API and provides a unified cross-platform interface for vision, text, and audio.Rather than providing a single model, MediaPipe ships a suite of ready-to-use Tasks that wrap trained models for specific problems. These include MediaPipe Pose Landmarker for 33-point body landmark detection, Hand Landmarker for 21-point hand tracking, Face Landmarker which extends the earlier 468-point Face Mesh with blendshape outputs for facial expression, Selfie Segmentation for person-background separation, and Holistic Landmarker for combined body, hand, and face tracking. The Tasks prioritize on-device inference with low latency and support GPU acceleration where available, making the framework a common choice for mobile augmented reality, fitness and wellness applications, gesture-based interfaces, and accessibility features such as sign language recognition.

Mask R-CNN is an instance segmentation model developed by Facebook AI Research (Meta), released in October 2017. It extends Faster R-CNN by adding a parallel branch that predicts binary segmentation masks for each detected object, independent of the classification and bounding box regression branches. A key contribution is RoIAlign, which replaces RoIPool with bilinear interpolation to preserve spatial correspondence between features and input pixels, significantly improving mask quality.Mask R-CNN achieves strong performance on the COCO instance segmentation benchmark and supports keypoint detection as an additional output head. It remains a foundational architecture in instance segmentation and is available through Meta's Detectron2 framework. The model is most appropriate for tasks requiring pixel-level object delineation, such as medical imaging, autonomous driving, and industrial inspection.

Detectron2 is a computer vision model library developed by Facebook AI Research (Meta), released in September 2019. It serves as a comprehensive platform for object detection, instance segmentation, panoptic segmentation, keypoint detection, and DensePose, implemented in PyTorch. It is the successor to the original Detectron framework, which was written in Caffe2, and offers a more modular and extensible codebase designed for both research and production use.Detectron2 includes implementations of Faster R-CNN, Mask R-CNN, RetinaNet, Cascade R-CNN, Panoptic FPN, and several other architectures. Its modular design allows components such as backbones, necks, and heads to be swapped independently, making it widely used as a baseline framework in academic research. It supports training on COCO-format datasets and integrates with standard distributed training setups.