File size: 3,530 Bytes
362605b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
---
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
|