Google: MobileNet SSD v2

YOLOv4-tiny is a lightweight variant of YOLOv4 developed by Academia Sinica, released in November 2020. It retains the core YOLOv4 design principles while significantly reducing the number of convolutional layers and feature map channels to produce a model suitable for inference on devices with limited compute, including embedded hardware and mobile CPUs. It uses a simplified CSP backbone with fewer layers and two detection scales rather than three.YOLOv4-tiny is optimized for scenarios where inference speed is prioritized over peak accuracy, achieving substantially higher FPS than full YOLOv4 at the cost of reduced AP on standard benchmarks. It is commonly used in robotics, embedded vision systems, and applications where real-time detection is required without GPU acceleration.

RT-DETR (Real-Time Detection Transformer) is an object detection model developed by Baidu, released in April 2023 under the Apache 2.0 license. It is the first transformer-based real-time object detector, addressing the inference speed limitations of earlier DETR models through an efficient hybrid encoder that decouples intra-scale interaction and cross-scale fusion, enabling the model to process multi-scale features without the high computational overhead of standard transformer encoders.RT-DETR achieves 53.1% AP on COCO at 108 FPS on an NVIDIA T4 GPU for the RT-DETR-L variant, outperforming comparably sized YOLO detectors at similar speeds. It maintains end-to-end inference without non-maximum suppression, simplifying deployment pipelines. RT-DETR established the baseline for real-time transformer detection and has been extended by subsequent works including RF-DETR and RT-DETRv2.

RF-DETR is a real-time transformer-based object detection model developed by Roboflow, with code and weights first released in March 2025 under the Apache 2.0 license. It is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark, built on a DINOv2 vision transformer backbone with weight-sharing neural architecture search used to identify accuracy-latency trade-offs. The full family spans six sizes from Nano (30.5M parameters, 384×384 input) to 2XL (126.9M parameters, 880×880 input), with the accompanying research paper accepted to ICLR 2026.RF-DETR is designed for strong domain adaptability, achieving state-of-the-art performance on RF100-VL, a benchmark measuring generalization to real-world object detection tasks across diverse domains. It is deployable through Roboflow Inference and supports fine-tuning on custom datasets, making it well suited for domain-specific applications with limited training data.

YOLO-NAS is an object detection model developed by Deci AI, released in May 2023 as part of the super-gradients open-source training library. The architecture was generated using Deci's proprietary Neural Architecture Search technology, AutoNAC, which searches for network structures that balance accuracy and inference latency on target hardware. This produced three model sizes (small, medium, and large) featuring quantization-friendly blocks that reduce accuracy loss when converting weights to INT8 precision for deployment on edge devices and mobile hardware.YOLO-NAS achieves competitive accuracy-latency tradeoffs against YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on the Microsoft COCO benchmark at release, and ships with pretraining on Objects365 in addition to COCO. Note that YOLO-NAS uses a custom license: the surrounding super-gradients framework code is Apache-2.0, but the YOLO-NAS model weights are released under a separate non-commercial license that restricts production and commercial use. Teams evaluating YOLO-NAS for commercial applications should review the LICENSE.YOLONAS.md terms directly. Deci AI was acquired by NVIDIA in April 2024, and the super-gradients repository is no longer actively maintained by the original team. Users can still download and use the released weights, but no further updates or new variants are expected.

YOLOS (You Only Look at One Sequence) is a transformer-based object detection model widely distributed through Hugging Face Transformers, released in June 2021 under the MIT license. It applies a minimally adapted Vision Transformer to object detection by representing both the image and detection tokens as a flat sequence processed by standard multi-head self-attention, without convolutional components or feature pyramid networks. The architecture demonstrates that detection can be performed without region proposals or multi-scale feature fusion.YOLOS achieves moderate performance on COCO relative to purpose-built detectors, with its primary contribution being a demonstration of the transferability of ViT pre-training to detection tasks. It is most appropriate for research contexts exploring transformer-based detection architectures and for scenarios where architectural simplicity is preferred over peak accuracy.

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.