YOLOv4-tiny vs YOLOv7

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.

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.