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rfdetr-base
RF-DETR is a real-time, transformer-based object detection model architecture developed by Roboflow and released under the Apache 2.0 license.
RF-DETR is the first real-time model to exceed 60 AP on the Microsoft COCO benchmark alongside competitive performance at base sizes. It also achieves state-of-the-art performance on RF100-VL, an object detection benchmark that measures model domain adaptability to real world problems. RF-DETR is fastest and most accurate for its size when compared current real-time objection models.
RF-DETR is small enough to run on the edge using Inference, making it an ideal model for deployments that need both strong accuracy and real-time performance.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-nano
RF-DETR Nano object detection model, served via the native rfdetr.cpp backend (ggml + purego, no Python).
Q8_0 quantization is the recommended default for CPU: same accuracy as F16/F32, ~20MB on disk, fastest CPU latency.
Pure C++/ggml runtime; no Python dependencies. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-base
RF-DETR Base object detection model, served via the native rfdetr.cpp backend.
F16 quantization is recommended on CPU: identical accuracy to F32, half the size, fastest.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-small
RF-DETR Small object detection model (DINOv2-small backbone, 512px input, 3 decoder layers), served
via the native rfdetr.cpp backend (ggml + purego, no Python). A step up from Nano in accuracy while
staying lightweight on CPU. F16 quantization is the recommended default: identical accuracy to F32
at roughly half the size. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-medium
RF-DETR Medium object detection model (DINOv2-small backbone, 576px input, 4 decoder layers), served
via the native rfdetr.cpp backend. Balanced detection quality vs. CPU latency — recommended when
Base is not accurate enough but Large is too slow. F16 quantization is the recommended default:
identical accuracy to F32, half the size. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-large
RF-DETR Large object detection model (DINOv2-small backbone, 704px input, 4 decoder layers), served
via the native rfdetr.cpp backend. Highest-accuracy detection variant — best for offline workflows
and high-resolution inputs where CPU latency is secondary to recall. F16 quantization is the
recommended default: identical accuracy to F32, half the size. Drop-in for the /v1/detection endpoint.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-nano
RF-DETR Seg-Nano instance segmentation model (DINOv2-small backbone, 312px input, 4 decoder layers,
100 queries), served via the native rfdetr.cpp backend. Smallest segmentation variant — fastest CPU
latency, ideal for edge deployment. Returns both bounding boxes and per-instance masks via the
/v1/detection endpoint. F16 quantization is the recommended default: identical accuracy to F32,
half the size.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-small
RF-DETR Seg-Small instance segmentation model (DINOv2-small backbone, 384px input, 4 decoder layers,
100 queries), served via the native rfdetr.cpp backend. Step up from Seg-Nano in mask quality while
staying CPU-friendly. Returns both bounding boxes and per-instance masks via the /v1/detection
endpoint. F16 quantization is the recommended default: identical accuracy to F32, half the size.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-medium
RF-DETR Seg-Medium instance segmentation model (DINOv2-small backbone, 432px input, 5 decoder layers,
200 queries), served via the native rfdetr.cpp backend. Balanced segmentation quality vs. CPU latency
— recommended for everyday segmentation workloads. Returns both bounding boxes and per-instance masks
via the /v1/detection endpoint. F16 quantization is the recommended default.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-large
RF-DETR Seg-Large instance segmentation model (DINOv2-small backbone, 504px input, 5 decoder layers,
200 queries), served via the native rfdetr.cpp backend. Higher-resolution input than Seg-Medium for
sharper mask boundaries. Returns both bounding boxes and per-instance masks via the /v1/detection
endpoint. F16 quantization is the recommended default: identical accuracy to F32, half the size.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-xlarge
RF-DETR Seg-XLarge instance segmentation model (DINOv2-small backbone, 624px input, 6 decoder layers,
300 queries), served via the native rfdetr.cpp backend. High-capacity segmentation variant with more
queries and deeper decoder — best for dense scenes with many instances. Returns both bounding boxes
and per-instance masks via the /v1/detection endpoint. F16 quantization is the recommended default.
Repository: localaiLicense: apache-2.0
rfdetr-cpp-seg-2xlarge
RF-DETR Seg-2XLarge instance segmentation model (DINOv2-small backbone, 768px input, 6 decoder layers,
300 queries), served via the native rfdetr.cpp backend. Highest-accuracy segmentation variant — best
for offline workflows and high-resolution inputs where CPU latency is secondary to mask quality.
Returns both bounding boxes and per-instance masks via the /v1/detection endpoint. F16 quantization
is the recommended default: identical accuracy to F32, half the size.
Repository: localaiLicense: apache-2.0