Create README.md

---
tags:
- object-detection
- instance-segmentation
- transformer
- detr
- npu
- qualcomm
- roboflow
- real-time
- edge-deployment
license: cc-by-nc-4.0
library_name: nexaSDK
---

# RF-DETR-Seg-Preview-NPU

Run RF-DETR-Seg-Preview on Qualcomm NPU with nexaSDK.

## Quickstart

Install nexaSDK and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai)

Activate your device with your access token:

```bash
nexa config set license '<access_token>'
```

Run the model locally in one line:

```bash
nexa infer NexaAI/rf-detr-seg-preview-npu
```

## Model Description

RF-DETR-Seg-Preview is a real-time object detection and instance segmentation model developed by Roboflow, based on the Transformer architecture. It is the first real-time model to achieve over 60 AP on the COCO dataset, combining high accuracy with efficient inference performance.

The model leverages a DINOv2 visual backbone and a lightweight DETR design, providing excellent transfer learning capabilities and domain adaptability. Its end-to-end architecture eliminates the need for non-maximum suppression (NMS) and anchor boxes, simplifying the detection pipeline.

RF-DETR-Seg-Preview brings state-of-the-art detection and segmentation accuracy to real-time applications, making it ideal for edge deployment scenarios where both speed and precision are critical.

## Features

- **High-Accuracy Real-Time Detection & Segmentation**: Achieves 60.5 mAP on COCO dataset while maintaining real-time performance, suitable for applications requiring both speed and accuracy.

- **Domain Adaptability**: Through the DINOv2 backbone network, enables cross-domain transfer learning, suitable for complex scenarios such as aerial imagery and industrial applications.

- **Dynamic Resolution Support**: Supports multi-resolution training and inference, allowing precision-speed trade-offs without retraining.

- **Efficient Edge Deployment**: Optimized for Qualcomm NPU, providing fast inference and low latency on edge devices with limited resources.

- **End-to-End Architecture**: Eliminates the need for NMS and anchor boxes, simplifying the detection and segmentation pipeline.

- **Instance Segmentation**: Provides pixel-level segmentation masks for each detected object, enabling precise object boundary identification.

## Use Cases

- **Real-Time Video Analysis**: Fast object detection and instance segmentation in image or video streams, suitable for autonomous driving, security monitoring, and surveillance systems.

- **Edge Device Deployment**: Lightweight design enables deployment on mobile devices, embedded systems, and other edge devices with resource constraints.

- **Autonomous Systems**: Detection and segmentation of pedestrians, vehicles, and other objects for autonomous navigation and robotics.

- **Custom Dataset Fine-Tuning**: Supports fine-tuning on custom datasets to meet specific application requirements.

- **Production Environments**: Efficient deployment in production or research environments requiring real-time performance.

## Inputs and Outputs

**Input:**

- Image path

**Output:**

- Detection results including object classes, bounding box coordinates, and confidence scores

## License

All NPU-related components of this project are licensed under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) license.

Commercial licensing or usage rights must be obtained through a separate agreement. For inquiries regarding commercial use, please contact dev@nexa.ai

README.md

@@ -0,0 +1,84 @@
+---
+tags:
+  - object-detection
+  - instance-segmentation
+  - transformer
+  - detr
+  - npu
+  - qualcomm
+  - roboflow
+  - real-time
+  - edge-deployment
+license: cc-by-nc-4.0
+library_name: nexa-sdk
+---
+
+# RF-DETR-Seg-Preview-NPU
+
+Run RF-DETR-Seg-Preview on Qualcomm NPU with nexaSDK.
+
+## Quickstart
+
+Install nexaSDK and create a free account at [sdk.nexa.ai](https://sdk.nexa.ai)
+
+Activate your device with your access token:
+
+```bash
+nexa config set license '<access_token>'
+```
+
+Run the model locally in one line:
+
+```bash
+nexa infer NexaAI/rf-detr-seg-preview-npu
+```
+
+## Model Description
+
+RF-DETR-Seg-Preview is a real-time object detection and instance segmentation model developed by Roboflow, based on the Transformer architecture. It is the first real-time model to achieve over 60 AP on the COCO dataset, combining high accuracy with efficient inference performance.
+
+The model leverages a DINOv2 visual backbone and a lightweight DETR design, providing excellent transfer learning capabilities and domain adaptability. Its end-to-end architecture eliminates the need for non-maximum suppression (NMS) and anchor boxes, simplifying the detection pipeline.
+
+RF-DETR-Seg-Preview brings state-of-the-art detection and segmentation accuracy to real-time applications, making it ideal for edge deployment scenarios where both speed and precision are critical.
+
+## Features
+
+- **High-Accuracy Real-Time Detection & Segmentation**: Achieves 60.5 mAP on COCO dataset while maintaining real-time performance, suitable for applications requiring both speed and accuracy.
+
+- **Domain Adaptability**: Through the DINOv2 backbone network, enables cross-domain transfer learning, suitable for complex scenarios such as aerial imagery and industrial applications.
+
+- **Dynamic Resolution Support**: Supports multi-resolution training and inference, allowing precision-speed trade-offs without retraining.
+
+- **Efficient Edge Deployment**: Optimized for Qualcomm NPU, providing fast inference and low latency on edge devices with limited resources.
+
+- **End-to-End Architecture**: Eliminates the need for NMS and anchor boxes, simplifying the detection and segmentation pipeline.
+
+- **Instance Segmentation**: Provides pixel-level segmentation masks for each detected object, enabling precise object boundary identification.
+
+## Use Cases
+
+- **Real-Time Video Analysis**: Fast object detection and instance segmentation in image or video streams, suitable for autonomous driving, security monitoring, and surveillance systems.
+
+- **Edge Device Deployment**: Lightweight design enables deployment on mobile devices, embedded systems, and other edge devices with resource constraints.
+
+- **Autonomous Systems**: Detection and segmentation of pedestrians, vehicles, and other objects for autonomous navigation and robotics.
+
+- **Custom Dataset Fine-Tuning**: Supports fine-tuning on custom datasets to meet specific application requirements.
+
+- **Production Environments**: Efficient deployment in production or research environments requiring real-time performance.
+
+## Inputs and Outputs
+
+**Input:**
+
+- Image path
+
+**Output:**
+
+- Detection results including object classes, bounding box coordinates, and confidence scores
+
+## License
+
+All NPU-related components of this project are licensed under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) license.
+
+Commercial licensing or usage rights must be obtained through a separate agreement. For inquiries regarding commercial use, please contact dev@nexa.ai