Yolo-X: Optimized for Mobile Deployment

Real-time object detection optimized for mobile and edge

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

This model is an implementation of Yolo-X found here.

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

Model Details

Model Type: Model_use_case.object_detection
Model Stats:
- Model checkpoint: YoloX Medium
- Input resolution: 640x640
- Number of parameters: 25.3M
- Model size (float): 96.7 MB

Model	Precision	Device	Chipset	Target Runtime	Inference Time (ms)	Peak Memory Range (MB)	Primary Compute Unit	Target Model
Yolo-X	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	TFLITE	79.809 ms	0 - 75 MB	NPU	Yolo-X.tflite
Yolo-X	float	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN	75.122 ms	1 - 11 MB	NPU	Use Export Script
Yolo-X	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	TFLITE	34.789 ms	0 - 106 MB	NPU	Yolo-X.tflite
Yolo-X	float	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN	34.374 ms	5 - 52 MB	NPU	Use Export Script
Yolo-X	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	TFLITE	20.102 ms	0 - 18 MB	NPU	Yolo-X.tflite
Yolo-X	float	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN	15.609 ms	5 - 8 MB	NPU	Use Export Script
Yolo-X	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	TFLITE	26.709 ms	0 - 76 MB	NPU	Yolo-X.tflite
Yolo-X	float	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN	22.043 ms	0 - 13 MB	NPU	Use Export Script
Yolo-X	float	SA7255P ADP	Qualcomm® SA7255P	TFLITE	79.809 ms	0 - 75 MB	NPU	Yolo-X.tflite
Yolo-X	float	SA7255P ADP	Qualcomm® SA7255P	QNN	75.122 ms	1 - 11 MB	NPU	Use Export Script
Yolo-X	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	TFLITE	20.353 ms	0 - 16 MB	NPU	Yolo-X.tflite
Yolo-X	float	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN	15.579 ms	5 - 7 MB	NPU	Use Export Script
Yolo-X	float	SA8295P ADP	Qualcomm® SA8295P	TFLITE	37.164 ms	0 - 68 MB	NPU	Yolo-X.tflite
Yolo-X	float	SA8295P ADP	Qualcomm® SA8295P	QNN	28.667 ms	0 - 18 MB	NPU	Use Export Script
Yolo-X	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	TFLITE	20.652 ms	0 - 17 MB	NPU	Yolo-X.tflite
Yolo-X	float	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN	15.705 ms	5 - 8 MB	NPU	Use Export Script
Yolo-X	float	SA8775P ADP	Qualcomm® SA8775P	TFLITE	26.709 ms	0 - 76 MB	NPU	Yolo-X.tflite
Yolo-X	float	SA8775P ADP	Qualcomm® SA8775P	QNN	22.043 ms	0 - 13 MB	NPU	Use Export Script
Yolo-X	float	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	TFLITE	20.57 ms	0 - 16 MB	NPU	Yolo-X.tflite
Yolo-X	float	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	QNN	15.753 ms	5 - 22 MB	NPU	Use Export Script
Yolo-X	float	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	ONNX	20.337 ms	1 - 151 MB	NPU	Yolo-X.onnx
Yolo-X	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	TFLITE	15.229 ms	0 - 111 MB	NPU	Yolo-X.tflite
Yolo-X	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN	12.042 ms	5 - 54 MB	NPU	Use Export Script
Yolo-X	float	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	15.441 ms	5 - 69 MB	NPU	Yolo-X.onnx
Yolo-X	float	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	TFLITE	13.711 ms	0 - 79 MB	NPU	Yolo-X.tflite
Yolo-X	float	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	QNN	9.209 ms	5 - 51 MB	NPU	Use Export Script
Yolo-X	float	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	ONNX	14.343 ms	5 - 59 MB	NPU	Yolo-X.onnx
Yolo-X	float	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	15.95 ms	5 - 5 MB	NPU	Use Export Script
Yolo-X	float	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	20.752 ms	46 - 46 MB	NPU	Yolo-X.onnx
Yolo-X	w8a16	QCS8275 (Proxy)	Qualcomm® QCS8275 (Proxy)	QNN	24.942 ms	2 - 12 MB	NPU	Use Export Script
Yolo-X	w8a16	QCS8450 (Proxy)	Qualcomm® QCS8450 (Proxy)	QNN	15.586 ms	2 - 84 MB	NPU	Use Export Script
Yolo-X	w8a16	QCS8550 (Proxy)	Qualcomm® QCS8550 (Proxy)	QNN	10.29 ms	2 - 6 MB	NPU	Use Export Script
Yolo-X	w8a16	QCS9075 (Proxy)	Qualcomm® QCS9075 (Proxy)	QNN	10.703 ms	1 - 16 MB	NPU	Use Export Script
Yolo-X	w8a16	RB3 Gen 2 (Proxy)	Qualcomm® QCS6490 (Proxy)	QNN	53.241 ms	2 - 16 MB	NPU	Use Export Script
Yolo-X	w8a16	SA7255P ADP	Qualcomm® SA7255P	QNN	24.942 ms	2 - 12 MB	NPU	Use Export Script
Yolo-X	w8a16	SA8255 (Proxy)	Qualcomm® SA8255P (Proxy)	QNN	10.449 ms	2 - 5 MB	NPU	Use Export Script
Yolo-X	w8a16	SA8295P ADP	Qualcomm® SA8295P	QNN	14.409 ms	0 - 17 MB	NPU	Use Export Script
Yolo-X	w8a16	SA8650 (Proxy)	Qualcomm® SA8650P (Proxy)	QNN	10.548 ms	3 - 5 MB	NPU	Use Export Script
Yolo-X	w8a16	SA8775P ADP	Qualcomm® SA8775P	QNN	10.703 ms	1 - 16 MB	NPU	Use Export Script
Yolo-X	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	QNN	10.461 ms	2 - 17 MB	NPU	Use Export Script
Yolo-X	w8a16	Samsung Galaxy S23	Snapdragon® 8 Gen 2 Mobile	ONNX	18.693 ms	1 - 48 MB	NPU	Yolo-X.onnx
Yolo-X	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	QNN	6.951 ms	2 - 80 MB	NPU	Use Export Script
Yolo-X	w8a16	Samsung Galaxy S24	Snapdragon® 8 Gen 3 Mobile	ONNX	13.603 ms	2 - 103 MB	NPU	Yolo-X.onnx
Yolo-X	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	QNN	5.952 ms	2 - 73 MB	NPU	Use Export Script
Yolo-X	w8a16	Snapdragon 8 Elite QRD	Snapdragon® 8 Elite Mobile	ONNX	14.152 ms	4 - 90 MB	NPU	Yolo-X.onnx
Yolo-X	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	QNN	11.179 ms	2 - 2 MB	NPU	Use Export Script
Yolo-X	w8a16	Snapdragon X Elite CRD	Snapdragon® X Elite	ONNX	20.376 ms	24 - 24 MB	NPU	Yolo-X.onnx

Installation

Install the package via pip:

pip install "qai-hub-models[yolox]"

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.yolox.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.yolox.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.yolox.export

Profiling Results
------------------------------------------------------------
Yolo-X
Device                          : cs_8275 (ANDROID 14)                 
Runtime                         : TFLITE                               
Estimated inference time (ms)   : 79.8                                 
Estimated peak memory usage (MB): [0, 75]                              
Total # Ops                     : 418                                  
Compute Unit(s)                 : npu (418 ops) gpu (0 ops) cpu (0 ops)

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.yolox 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.

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.yolox.demo --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.yolox.demo -- --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 Yolo-X's performance across various devices here. Explore all available models on Qualcomm® AI Hub

License

The license for the original implementation of Yolo-X 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.