EfficientDet vs YOLOv8

EfficientDet is an object detection model developed by Google Research, released in November 2019. It introduces a compound scaling method that uniformly scales the resolution, depth, and width of the detection network, building on the EfficientNet backbone and a bidirectional feature pyramid network (BiFPN) for multi-scale feature fusion. This design achieves strong accuracy-efficiency tradeoffs across a family of models ranging from EfficientDet-D0 to D7.

EfficientDet-D7 achieves 55.1% AP on COCO while remaining significantly smaller in parameter count than comparable models at the time of release. The model family is well suited for deployment scenarios where compute budget varies, as smaller variants can run on edge hardware while larger variants are competitive with heavier architectures on server-side 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.