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YOLOv10-Detection

Object Detection
PyTorch
real_time
android
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 ---
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/yolov10_det/web-assets/model_demo.png)
# YOLOv10-Detection: Optimized for Mobile Deployment
## Real-time object detection optimized for mobile and edge by Ultralytics
Ultralytics YOLOv10 is a machine learning model that predicts bounding boxes and classes of objects in an image.
This model is an implementation of YOLOv10-Detection found [here](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect).
This repository provides scripts to run YOLOv10-Detection on Qualcomm® devices.
More details on model performance across various devices, can be found
[here](https://aihub.qualcomm.com/models/yolov10_det).
**WARNING**: The model assets are not readily available for download due to licensing restrictions.
### Model Details
- **Model Type:** Model_use_case.object_detection
- **Model Stats:**
- Model checkpoint: YOLOv10-N
- Input resolution: 640x640
- Number of parameters: 2.33M
- Model size (float): 8.95 MB
- Model size (w8a8): 2.55 MB
- Model size (w8a16): 3.04 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| YOLOv10-Detection | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 16.29 ms | 0 - 41 MB | NPU | -- |
| YOLOv10-Detection | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 12.906 ms | 1 - 99 MB | NPU | -- |
| YOLOv10-Detection | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 9.095 ms | 0 - 40 MB | NPU | -- |
| YOLOv10-Detection | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 9.1 ms | 5 - 45 MB | NPU | -- |
| YOLOv10-Detection | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 6.01 ms | 0 - 19 MB | NPU | -- |
| YOLOv10-Detection | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 3.942 ms | 0 - 72 MB | NPU | -- |
| YOLOv10-Detection | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 7.407 ms | 0 - 41 MB | NPU | -- |
| YOLOv10-Detection | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 5.461 ms | 2 - 104 MB | NPU | -- |
| YOLOv10-Detection | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 16.29 ms | 0 - 41 MB | NPU | -- |
| YOLOv10-Detection | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 12.906 ms | 1 - 99 MB | NPU | -- |
| YOLOv10-Detection | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 6.023 ms | 0 - 19 MB | NPU | -- |
| YOLOv10-Detection | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 3.978 ms | 0 - 59 MB | NPU | -- |
| YOLOv10-Detection | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 10.425 ms | 0 - 27 MB | NPU | -- |
| YOLOv10-Detection | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 8.267 ms | 3 - 37 MB | NPU | -- |
| YOLOv10-Detection | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 6.115 ms | 0 - 19 MB | NPU | -- |
| YOLOv10-Detection | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 3.966 ms | 0 - 61 MB | NPU | -- |
| YOLOv10-Detection | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 7.407 ms | 0 - 41 MB | NPU | -- |
| YOLOv10-Detection | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 5.461 ms | 2 - 104 MB | NPU | -- |
| YOLOv10-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 6.029 ms | 0 - 10 MB | NPU | -- |
| YOLOv10-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 3.953 ms | 0 - 67 MB | NPU | -- |
| YOLOv10-Detection | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 5.742 ms | 0 - 82 MB | NPU | -- |
| YOLOv10-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 4.365 ms | 0 - 57 MB | NPU | -- |
| YOLOv10-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.794 ms | 5 - 215 MB | NPU | -- |
| YOLOv10-Detection | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 4.167 ms | 0 - 162 MB | NPU | -- |
| YOLOv10-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 3.46 ms | 0 - 47 MB | NPU | -- |
| YOLOv10-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.668 ms | 5 - 133 MB | NPU | -- |
| YOLOv10-Detection | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 3.602 ms | 5 - 88 MB | NPU | -- |
| YOLOv10-Detection | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.592 ms | 5 - 5 MB | NPU | -- |
| YOLOv10-Detection | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 6.195 ms | 5 - 5 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 7.54 ms | 2 - 35 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 4.733 ms | 2 - 41 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 4.037 ms | 2 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 4.662 ms | 2 - 36 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 13.307 ms | 2 - 38 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 7.54 ms | 2 - 35 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 4.051 ms | 2 - 15 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 5.358 ms | 2 - 37 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 4.034 ms | 2 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 4.662 ms | 2 - 36 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 4.053 ms | 2 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 11.693 ms | 1 - 36 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 2.714 ms | 2 - 44 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 8.689 ms | 2 - 153 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 2.292 ms | 2 - 39 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 8.067 ms | 2 - 87 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.658 ms | 20 - 20 MB | NPU | -- |
| YOLOv10-Detection | w8a16 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 12.651 ms | 2 - 2 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 3.681 ms | 0 - 24 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 3.715 ms | 1 - 27 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 2.049 ms | 0 - 40 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 2.085 ms | 1 - 35 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 1.821 ms | 0 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 1.825 ms | 1 - 13 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 2.282 ms | 0 - 25 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 2.259 ms | 1 - 29 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 4.132 ms | 0 - 31 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 5.391 ms | 1 - 33 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 64.773 ms | 2 - 12 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 3.681 ms | 0 - 24 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 3.715 ms | 1 - 27 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 1.829 ms | 0 - 13 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 1.828 ms | 1 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 2.635 ms | 0 - 27 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 2.611 ms | 1 - 31 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 1.83 ms | 0 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 1.826 ms | 1 - 12 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 2.282 ms | 0 - 25 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 2.259 ms | 1 - 29 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 1.837 ms | 0 - 13 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 1.831 ms | 1 - 14 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 6.805 ms | 0 - 32 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 1.214 ms | 0 - 35 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 1.241 ms | 1 - 36 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 5.009 ms | 1 - 81 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 1.115 ms | 0 - 28 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 1.087 ms | 1 - 34 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 3.538 ms | 1 - 83 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 2.155 ms | 11 - 11 MB | NPU | -- |
| YOLOv10-Detection | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.466 ms | 1 - 1 MB | NPU | -- |
## Installation
Install the package via pip:
```bash
pip install "qai-hub-models[yolov10-det]"
```
## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`.
With this API token, you can configure your client to run models on the cloud
hosted devices.
```bash
qai-hub configure --api_token API_TOKEN
```
Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information.
## Demo off target
The package contains a simple end-to-end demo that downloads pre-trained
weights and runs this model on a sample input.
```bash
python -m qai_hub_models.models.yolov10_det.demo
```
The above demo runs a reference implementation of pre-processing, model
inference, and post processing.
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov10_det.demo
```
### Run model on a cloud-hosted device
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
device. This script does the following:
* Performance check on-device on a cloud-hosted device
* Downloads compiled assets that can be deployed on-device for Android.
* Accuracy check between PyTorch and on-device outputs.
```bash
python -m qai_hub_models.models.yolov10_det.export
```
## How does this work?
This [export script](https://aihub.qualcomm.com/models/yolov10_det/qai_hub_models/models/YOLOv10-Detection/export.py)
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
on-device. Lets go through each step below in detail:
Step 1: **Compile model for on-device deployment**
To compile a PyTorch model for on-device deployment, we first trace the model
in memory using the `jit.trace` and then call the `submit_compile_job` API.
```python
import torch
import qai_hub as hub
from qai_hub_models.models.yolov10_det import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S24")
# Trace model
input_shape = torch_model.get_input_spec()
sample_inputs = torch_model.sample_inputs()
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
# Compile model on a specific device
compile_job = hub.submit_compile_job(
model=pt_model,
device=device,
input_specs=torch_model.get_input_spec(),
)
# Get target model to run on-device
target_model = compile_job.get_target_model()
```
Step 2: **Performance profiling on cloud-hosted device**
After compiling models from step 1. Models can be profiled model on-device using the
`target_model`. Note that this scripts runs the model on a device automatically
provisioned in the cloud. Once the job is submitted, you can navigate to a
provided job URL to view a variety of on-device performance metrics.
```python
profile_job = hub.submit_profile_job(
model=target_model,
device=device,
)
```
Step 3: **Verify on-device accuracy**
To verify the accuracy of the model on-device, you can run on-device inference
on sample input data on the same cloud hosted device.
```python
input_data = torch_model.sample_inputs()
inference_job = hub.submit_inference_job(
model=target_model,
device=device,
inputs=input_data,
)
on_device_output = inference_job.download_output_data()
```
With the output of the model, you can compute like PSNR, relative errors or
spot check the output with expected output.
**Note**: This on-device profiling and inference requires access to Qualcomm®
AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
## Run demo on a cloud-hosted device
You can also run the demo on-device.
```bash
python -m qai_hub_models.models.yolov10_det.demo --eval-mode on-device
```
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
environment, please add the following to your cell (instead of the above).
```
%run -m qai_hub_models.models.yolov10_det.demo -- --eval-mode on-device
```
## Deploying compiled model to Android
The models can be deployed using multiple runtimes:
- TensorFlow Lite (`.tflite` export): [This
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
guide to deploy the .tflite model in an Android application.
- QNN (`.so` export ): This [sample
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
provides instructions on how to use the `.so` shared library in an Android application.
## View on Qualcomm® AI Hub
Get more details on YOLOv10-Detection's performance across various devices [here](https://aihub.qualcomm.com/models/yolov10_det).
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
## License
* The license for the original implementation of YOLOv10-Detection can be found
[here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://github.com/ultralytics/ultralytics/blob/main/LICENSE)
## References
* [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458)
* [Source Model Implementation](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/models/yolo/detect)
## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).