README.md

---
library_name: pytorch
license: other
tags:
- real_time
- android
pipeline_tag: object-detection

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rtmdet/web-assets/model_demo.png)

# RTMDet: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge


RTMDet is a highly efficient model for real-time object detection,capable of predicting both the bounding boxes and classes of objects within an image.It is highly optimized for real-time applications, making it reliable for industrial and commercial use

This model is an implementation of RTMDet found [here](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet).


 More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/rtmdet).

### Model Details

- **Model Type:** Model_use_case.object_detection
- **Model Stats:**
  - Model checkpoint: RTMDet Medium
  - Input resolution: 640x640
  - Number of parameters: 27.5M
  - Model size (float): 105 MB

| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| RTMDet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 85.266 ms | 0 - 63 MB | NPU | -- |
| RTMDet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 32.519 ms | 0 - 111 MB | NPU | -- |
| RTMDet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 15.695 ms | 0 - 17 MB | NPU | -- |
| RTMDet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 23.681 ms | 0 - 63 MB | NPU | -- |
| RTMDet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 85.266 ms | 0 - 63 MB | NPU | -- |
| RTMDet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 16.077 ms | 0 - 14 MB | NPU | -- |
| RTMDet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 35.604 ms | 0 - 73 MB | NPU | -- |
| RTMDet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 16.216 ms | 0 - 13 MB | NPU | -- |
| RTMDet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 23.681 ms | 0 - 63 MB | NPU | -- |
| RTMDet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 16.224 ms | 0 - 14 MB | NPU | -- |
| RTMDet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 14.484 ms | 1 - 141 MB | NPU | -- |
| RTMDet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 12.277 ms | 0 - 102 MB | NPU | -- |
| RTMDet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 11.001 ms | 4 - 50 MB | NPU | -- |
| RTMDet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 11.391 ms | 0 - 69 MB | NPU | -- |
| RTMDet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 10.193 ms | 7 - 47 MB | NPU | -- |
| RTMDet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 15.801 ms | 51 - 51 MB | NPU | -- |




## License
* The license for the original implementation of RTMDet can be found
  [here](https://github.com/open-mmlab/mmdetection/blob/3.x/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)



## References
* [RTMDet: An Empirical Study of Designing Real-Time Object Detectors](https://github.com/open-mmlab/mmdetection/blob/3.x/README.md)
* [Source Model Implementation](https://github.com/open-mmlab/mmdetection/tree/3.x/configs/rtmdet)



## Community
* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:[email protected]).

## Usage and Limitations

Model may not be used for or in connection with any of the following applications:

- Accessing essential private and public services and benefits;
- Administration of justice and democratic processes;
- Assessing or recognizing the emotional state of a person;
- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
- Education and vocational training;
- Employment and workers management;
- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
- General purpose social scoring;
- Law enforcement;
- Management and operation of critical infrastructure;
- Migration, asylum and border control management;
- Predictive policing;
- Real-time remote biometric identification in public spaces;
- Recommender systems of social media platforms;
- Scraping of facial images (from the internet or otherwise); and/or
- Subliminal manipulation