SAM 3 vs YOLOv4-tiny

Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.

Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.

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.