Academia Sinica: YOLOv4-tiny

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.

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.

YOLOv5 is an object detection model developed by Ultralytics, released in June 2020 under the AGPL-3.0 license. It is implemented in PyTorch and introduced a more accessible and well-documented YOLO implementation compared to earlier Darknet-based versions, with an integrated training and export pipeline supporting a wide range of deployment targets. YOLOv5 uses a CSP backbone, PANet neck, and a single-stage detection head with anchor-based regression.YOLOv5 is available in five sizes from Nano to Extra Large and supports export to ONNX, TensorRT, CoreML, and other formats. It is one of the most widely deployed object detection models in production environments and remains a common starting point for custom detection model training due to its documentation, community support, and compatibility with Roboflow Inference.

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.

MobileNet SSD v2 is a lightweight object detection model developed by Google Research, released in January 2018. It combines the MobileNetV2 backbone with the Single Shot MultiBox Detector (SSD) framework to produce a model optimized for inference on mobile and edge devices. MobileNetV2 introduces inverted residuals and linear bottlenecks to reduce computation while maintaining representational capacity compared to its predecessor.MobileNet SSD v2 is designed for real-time on-device detection, making it suitable for mobile apps, embedded systems, and IoT devices. It performs object detection across a fixed set of categories and can be fine-tuned on custom datasets. It trades peak accuracy for reduced inference cost and model size relative to larger two-stage detectors.