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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
edgetam
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).
Repository: localaiLicense: apache-2.0