EfficientDet vs RF-DETR

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.

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.