YOLOv8-Detection: Optimized for Mobile Deployment

Real-time object detection optimized for mobile and edge by Ultralytics

Ultralytics YOLOv8 is a machine learning model that predicts bounding boxes and classes of objects in an image.

This model is an implementation of YOLOv8-Detection found here.

This repository provides scripts to run YOLOv8-Detection on Qualcomm® devices. More details on model performance across various devices, can be found here.

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: YOLOv8-N
- Input resolution: 640x640
- Number of parameters: 3.18M
- Model size (float): 12.2 MB
- Model size (w8a8): 3.25 MB
- Model size (w8a16): 3.60 MB

Model	Precision	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit	Target Model
YOLOv8-Detection	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	13.467 ms	0 - 81 MB	NPU	--
YOLOv8-Detection	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	13.009 ms	0 - 120 MB	NPU	--
YOLOv8-Detection	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	6.131 ms	0 - 40 MB	NPU	--
YOLOv8-Detection	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	7.56 ms	5 - 41 MB	NPU	--
YOLOv8-Detection	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	3.473 ms	0 - 82 MB	NPU	--
YOLOv8-Detection	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	3.301 ms	0 - 66 MB	NPU	--
YOLOv8-Detection	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	5.338 ms	0 - 59 MB	NPU	--
YOLOv8-Detection	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	20.509 ms	0 - 81 MB	NPU	--
YOLOv8-Detection	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	4.904 ms	1 - 105 MB	NPU	--
YOLOv8-Detection	float	SA7255P ADP	Qualcomm® SA7255P	TFLITE	13.467 ms	0 - 81 MB	NPU	--
YOLOv8-Detection	float	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	13.009 ms	0 - 120 MB	NPU	--
YOLOv8-Detection	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	3.488 ms	0 - 88 MB	NPU	--
YOLOv8-Detection	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	3.287 ms	5 - 83 MB	NPU	--
YOLOv8-Detection	float	SA8295P ADP	Qualcomm® SA8295P	TFLITE	7.219 ms	0 - 34 MB	NPU	--
YOLOv8-Detection	float	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	7.175 ms	0 - 32 MB	NPU	--
YOLOv8-Detection	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	3.484 ms	0 - 87 MB	NPU	--
YOLOv8-Detection	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	3.296 ms	5 - 79 MB	NPU	--
YOLOv8-Detection	float	SA8775P ADP	Qualcomm® SA8775P	TFLITE	20.509 ms	0 - 81 MB	NPU	--
YOLOv8-Detection	float	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	4.904 ms	1 - 105 MB	NPU	--
YOLOv8-Detection	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	2.586 ms	0 - 162 MB	NPU	--
YOLOv8-Detection	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	2.453 ms	5 - 261 MB	NPU	--
YOLOv8-Detection	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	3.454 ms	0 - 102 MB	NPU	--
YOLOv8-Detection	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	TFLITE	2.039 ms	0 - 87 MB	NPU	--
YOLOv8-Detection	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	1.862 ms	5 - 107 MB	NPU	--
YOLOv8-Detection	float	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	2.905 ms	2 - 85 MB	NPU	--
YOLOv8-Detection	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	TFLITE	1.565 ms	0 - 82 MB	NPU	--
YOLOv8-Detection	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	1.461 ms	5 - 128 MB	NPU	--
YOLOv8-Detection	float	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	2.422 ms	1 - 69 MB	NPU	--
YOLOv8-Detection	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	3.702 ms	141 - 141 MB	NPU	--
YOLOv8-Detection	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	5.607 ms	5 - 5 MB	NPU	--
YOLOv8-Detection	w8a16	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	6.289 ms	2 - 32 MB	NPU	--
YOLOv8-Detection	w8a16	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	3.948 ms	2 - 43 MB	NPU	--
YOLOv8-Detection	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	3.173 ms	2 - 13 MB	NPU	--
YOLOv8-Detection	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	5.112 ms	1 - 56 MB	NPU	--
YOLOv8-Detection	w8a16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	3.762 ms	2 - 31 MB	NPU	--
YOLOv8-Detection	w8a16	RB3 Gen 2 (Proxy)	Qualcomm® QCS6490 (Proxy)	ONNX	164.771 ms	65 - 79 MB	CPU	--
YOLOv8-Detection	w8a16	RB5 (Proxy)	Qualcomm® QCS8250 (Proxy)	ONNX	136.502 ms	62 - 66 MB	CPU	--
YOLOv8-Detection	w8a16	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	6.289 ms	2 - 32 MB	NPU	--
YOLOv8-Detection	w8a16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	3.18 ms	1 - 13 MB	NPU	--
YOLOv8-Detection	w8a16	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	4.345 ms	2 - 41 MB	NPU	--
YOLOv8-Detection	w8a16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	3.185 ms	2 - 13 MB	NPU	--
YOLOv8-Detection	w8a16	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	3.762 ms	2 - 31 MB	NPU	--
YOLOv8-Detection	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	2.105 ms	2 - 38 MB	NPU	--
YOLOv8-Detection	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	3.068 ms	0 - 106 MB	NPU	--
YOLOv8-Detection	w8a16	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	1.476 ms	2 - 38 MB	NPU	--
YOLOv8-Detection	w8a16	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	2.392 ms	0 - 76 MB	NPU	--
YOLOv8-Detection	w8a16	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	1.201 ms	2 - 40 MB	NPU	--
YOLOv8-Detection	w8a16	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	2.147 ms	1 - 78 MB	NPU	--
YOLOv8-Detection	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	3.568 ms	5 - 5 MB	NPU	--
YOLOv8-Detection	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	5.257 ms	2 - 2 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	3.182 ms	0 - 24 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	3.047 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	1.585 ms	0 - 35 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN_DLC	1.595 ms	1 - 33 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	1.416 ms	0 - 14 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	1.308 ms	1 - 15 MB	NPU	--
YOLOv8-Detection	w8a8	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	ONNX	2.196 ms	0 - 45 MB	NPU	--
YOLOv8-Detection	w8a8	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	1.865 ms	0 - 24 MB	NPU	--
YOLOv8-Detection	w8a8	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	1.678 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8	RB3 Gen 2 (Proxy)	Qualcomm® QCS6490 (Proxy)	TFLITE	3.697 ms	0 - 32 MB	NPU	--
YOLOv8-Detection	w8a8	RB3 Gen 2 (Proxy)	Qualcomm® QCS6490 (Proxy)	QNN_DLC	4.409 ms	0 - 33 MB	NPU	--
YOLOv8-Detection	w8a8	RB3 Gen 2 (Proxy)	Qualcomm® QCS6490 (Proxy)	ONNX	40.749 ms	22 - 38 MB	CPU	--
YOLOv8-Detection	w8a8	RB5 (Proxy)	Qualcomm® QCS8250 (Proxy)	TFLITE	43.901 ms	3 - 11 MB	NPU	--
YOLOv8-Detection	w8a8	RB5 (Proxy)	Qualcomm® QCS8250 (Proxy)	ONNX	36.395 ms	21 - 27 MB	CPU	--
YOLOv8-Detection	w8a8	SA7255P ADP	Qualcomm® SA7255P	TFLITE	3.182 ms	0 - 24 MB	NPU	--
YOLOv8-Detection	w8a8	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	3.047 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	1.41 ms	0 - 15 MB	NPU	--
YOLOv8-Detection	w8a8	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	1.311 ms	1 - 16 MB	NPU	--
YOLOv8-Detection	w8a8	SA8295P ADP	Qualcomm® SA8295P	TFLITE	2.245 ms	0 - 31 MB	NPU	--
YOLOv8-Detection	w8a8	SA8295P ADP	Qualcomm® SA8295P	QNN_DLC	2.073 ms	1 - 31 MB	NPU	--
YOLOv8-Detection	w8a8	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	1.412 ms	0 - 15 MB	NPU	--
YOLOv8-Detection	w8a8	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	1.311 ms	1 - 16 MB	NPU	--
YOLOv8-Detection	w8a8	SA8775P ADP	Qualcomm® SA8775P	TFLITE	1.865 ms	0 - 24 MB	NPU	--
YOLOv8-Detection	w8a8	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	1.678 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	0.935 ms	0 - 36 MB	NPU	--
YOLOv8-Detection	w8a8	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	0.899 ms	1 - 32 MB	NPU	--
YOLOv8-Detection	w8a8	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	1.465 ms	0 - 147 MB	NPU	--
YOLOv8-Detection	w8a8	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	TFLITE	0.729 ms	0 - 28 MB	NPU	--
YOLOv8-Detection	w8a8	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	0.644 ms	1 - 29 MB	NPU	--
YOLOv8-Detection	w8a8	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	ONNX	1.09 ms	0 - 81 MB	NPU	--
YOLOv8-Detection	w8a8	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	TFLITE	0.652 ms	0 - 29 MB	NPU	--
YOLOv8-Detection	w8a8	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	0.583 ms	1 - 35 MB	NPU	--
YOLOv8-Detection	w8a8	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	ONNX	0.956 ms	0 - 85 MB	NPU	--
YOLOv8-Detection	w8a8	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	1.546 ms	2 - 2 MB	NPU	--
YOLOv8-Detection	w8a8	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	2.192 ms	2 - 2 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN_DLC	4.367 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN_DLC	2.035 ms	2 - 12 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN_DLC	2.544 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	SA7255P ADP	Qualcomm® SA7255P	QNN_DLC	4.367 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN_DLC	2.037 ms	2 - 12 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN_DLC	2.04 ms	2 - 12 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	SA8775P ADP	Qualcomm® SA8775P	QNN_DLC	2.544 ms	1 - 25 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN_DLC	1.369 ms	2 - 38 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	Samsung Galaxy S25	Snapdragon® 8 Elite For Galaxy Mobile	QNN_DLC	0.984 ms	2 - 36 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	Snapdragon 8 Elite Gen 5 QRD	Snapdragon® 8 Elite Gen5 Mobile	QNN_DLC	0.819 ms	2 - 32 MB	NPU	--
YOLOv8-Detection	w8a8_mixed_int16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN_DLC	2.318 ms	2 - 2 MB	NPU	--

Installation

Install the package via pip:

pip install "qai-hub-models[yolov8-det]"

Configure Qualcomm® AI Hub to run this model on a cloud-hosted device

Sign-in to Qualcomm® AI Hub 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.

qai-hub configure --api_token API_TOKEN

Navigate to 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.

python -m qai_hub_models.models.yolov8_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.yolov8_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.

python -m qai_hub_models.models.yolov8_det.export

How does this work?

This export script leverages Qualcomm® AI Hub 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.

import torch

import qai_hub as hub
from qai_hub_models.models.yolov8_det import Model

# Load the model
torch_model = Model.from_pretrained()

# Device
device = hub.Device("Samsung Galaxy S25")

# 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.

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.

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.

Run demo on a cloud-hosted device

You can also run the demo on-device.

python -m qai_hub_models.models.yolov8_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.yolov8_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 provides a guide to deploy the .tflite model in an Android application.
QNN (.so export ): This sample app provides instructions on how to use the .so shared library in an Android application.

View on Qualcomm® AI Hub

Get more details on YOLOv8-Detection's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of YOLOv8-Detection can be found here.
The license for the compiled assets for on-device deployment can be found here

References

Community

Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
For questions or feedback please reach out to us.

Downloads last month: -; Downloads are not tracked for this model. How to track