Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
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.
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Repository: localaiLicense: apache-2.0
EdgeTAM is an ultra-efficient variant of the Segment Anything Model (SAM) for image segmentation. It uses a RepViT backbone and is only ~16MB quantized (Q4_0), making it ideal for edge deployment. Supports point-prompted and box-prompted image segmentation via the /v1/detection endpoint. Powered by sam3.cpp (C/C++ with GGML).
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