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Yolo-X

Object Detection
PyTorch
LiteRT
ONNX
real_time
android
Model card Files Community

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 Small
    • Input resolution: 640x640
    • Number of parameters: 8.98M
    • Model size: 34.3 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 37.13 ms 0 - 35 MB NPU Yolo-X.tflite
Yolo-X float QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 31.417 ms 1 - 10 MB NPU Use Export Script
Yolo-X float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 20.928 ms 0 - 52 MB NPU Yolo-X.tflite
Yolo-X float QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 17.606 ms 5 - 42 MB NPU Use Export Script
Yolo-X float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 13.884 ms 0 - 11 MB NPU Yolo-X.tflite
Yolo-X float QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 8.418 ms 5 - 8 MB NPU Use Export Script
Yolo-X float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 16.992 ms 0 - 35 MB NPU Yolo-X.tflite
Yolo-X float QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 11.217 ms 1 - 12 MB NPU Use Export Script
Yolo-X float SA7255P ADP Qualcomm® SA7255P TFLITE 37.13 ms 0 - 35 MB NPU Yolo-X.tflite
Yolo-X float SA7255P ADP Qualcomm® SA7255P QNN 31.417 ms 1 - 10 MB NPU Use Export Script
Yolo-X float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 14.025 ms 0 - 9 MB NPU Yolo-X.tflite
Yolo-X float SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 8.136 ms 5 - 7 MB NPU Use Export Script
Yolo-X float SA8295P ADP Qualcomm® SA8295P TFLITE 21.889 ms 0 - 39 MB NPU Yolo-X.tflite
Yolo-X float SA8295P ADP Qualcomm® SA8295P QNN 14.516 ms 0 - 17 MB NPU Use Export Script
Yolo-X float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 14.19 ms 0 - 10 MB NPU Yolo-X.tflite
Yolo-X float SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 8.154 ms 5 - 8 MB NPU Use Export Script
Yolo-X float SA8775P ADP Qualcomm® SA8775P TFLITE 16.992 ms 0 - 35 MB NPU Yolo-X.tflite
Yolo-X float SA8775P ADP Qualcomm® SA8775P QNN 11.217 ms 1 - 12 MB NPU Use Export Script
Yolo-X float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 14.01 ms 0 - 10 MB NPU Yolo-X.tflite
Yolo-X float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 8.144 ms 5 - 21 MB NPU Use Export Script
Yolo-X float Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 13.272 ms 1 - 63 MB NPU Yolo-X.onnx
Yolo-X float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 9.942 ms 0 - 47 MB NPU Yolo-X.tflite
Yolo-X float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 5.963 ms 5 - 79 MB NPU Use Export Script
Yolo-X float Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 9.42 ms 5 - 155 MB NPU Yolo-X.onnx
Yolo-X float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 9.198 ms 0 - 39 MB NPU Yolo-X.tflite
Yolo-X float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 5.86 ms 5 - 77 MB NPU Use Export Script
Yolo-X float Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 8.709 ms 5 - 104 MB NPU Yolo-X.onnx
Yolo-X float Snapdragon X Elite CRD Snapdragon® X Elite QNN 8.937 ms 5 - 5 MB NPU Use Export Script
Yolo-X float Snapdragon X Elite CRD Snapdragon® X Elite ONNX 13.402 ms 15 - 15 MB NPU Yolo-X.onnx
Yolo-X w8a16 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 15.46 ms 1 - 11 MB NPU Use Export Script
Yolo-X w8a16 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 9.385 ms 2 - 50 MB NPU Use Export Script
Yolo-X w8a16 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 7.644 ms 2 - 5 MB NPU Use Export Script
Yolo-X w8a16 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 8.22 ms 2 - 13 MB NPU Use Export Script
Yolo-X w8a16 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 26.482 ms 2 - 14 MB NPU Use Export Script
Yolo-X w8a16 SA7255P ADP Qualcomm® SA7255P QNN 15.46 ms 1 - 11 MB NPU Use Export Script
Yolo-X w8a16 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 7.698 ms 2 - 5 MB NPU Use Export Script
Yolo-X w8a16 SA8295P ADP Qualcomm® SA8295P QNN 9.907 ms 0 - 17 MB NPU Use Export Script
Yolo-X w8a16 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 7.663 ms 3 - 5 MB NPU Use Export Script
Yolo-X w8a16 SA8775P ADP Qualcomm® SA8775P QNN 8.22 ms 2 - 13 MB NPU Use Export Script
Yolo-X w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 7.758 ms 2 - 13 MB NPU Use Export Script
Yolo-X w8a16 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 14.158 ms 0 - 39 MB NPU Yolo-X.onnx
Yolo-X w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 5.057 ms 2 - 51 MB NPU Use Export Script
Yolo-X w8a16 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 10.695 ms 2 - 132 MB NPU Yolo-X.onnx
Yolo-X w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 4.844 ms 2 - 51 MB NPU Use Export Script
Yolo-X w8a16 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 12.031 ms 2 - 83 MB NPU Yolo-X.onnx
Yolo-X w8a16 Snapdragon X Elite CRD Snapdragon® X Elite QNN 8.365 ms 2 - 2 MB NPU Use Export Script
Yolo-X w8a16 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 15.226 ms 8 - 8 MB NPU Yolo-X.onnx
Yolo-X w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) TFLITE 6.405 ms 0 - 28 MB NPU Yolo-X.tflite
Yolo-X w8a8 QCS8275 (Proxy) Qualcomm® QCS8275 (Proxy) QNN 5.381 ms 1 - 11 MB NPU Use Export Script
Yolo-X w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) TFLITE 3.154 ms 0 - 50 MB NPU Yolo-X.tflite
Yolo-X w8a8 QCS8450 (Proxy) Qualcomm® QCS8450 (Proxy) QNN 2.884 ms 1 - 46 MB NPU Use Export Script
Yolo-X w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) TFLITE 2.873 ms 0 - 35 MB NPU Yolo-X.tflite
Yolo-X w8a8 QCS8550 (Proxy) Qualcomm® QCS8550 (Proxy) QNN 2.277 ms 1 - 3 MB NPU Use Export Script
Yolo-X w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) TFLITE 3.265 ms 0 - 29 MB NPU Yolo-X.tflite
Yolo-X w8a8 QCS9075 (Proxy) Qualcomm® QCS9075 (Proxy) QNN 2.642 ms 1 - 13 MB NPU Use Export Script
Yolo-X w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) TFLITE 7.994 ms 0 - 43 MB NPU Yolo-X.tflite
Yolo-X w8a8 RB3 Gen 2 (Proxy) Qualcomm® QCS6490 (Proxy) QNN 9.89 ms 1 - 12 MB NPU Use Export Script
Yolo-X w8a8 SA7255P ADP Qualcomm® SA7255P TFLITE 6.405 ms 0 - 28 MB NPU Yolo-X.tflite
Yolo-X w8a8 SA7255P ADP Qualcomm® SA7255P QNN 5.381 ms 1 - 11 MB NPU Use Export Script
Yolo-X w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) TFLITE 2.884 ms 0 - 35 MB NPU Yolo-X.tflite
Yolo-X w8a8 SA8255 (Proxy) Qualcomm® SA8255P (Proxy) QNN 2.279 ms 0 - 2 MB NPU Use Export Script
Yolo-X w8a8 SA8295P ADP Qualcomm® SA8295P TFLITE 4.187 ms 0 - 30 MB NPU Yolo-X.tflite
Yolo-X w8a8 SA8295P ADP Qualcomm® SA8295P QNN 3.595 ms 1 - 18 MB NPU Use Export Script
Yolo-X w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) TFLITE 2.885 ms 0 - 33 MB NPU Yolo-X.tflite
Yolo-X w8a8 SA8650 (Proxy) Qualcomm® SA8650P (Proxy) QNN 2.278 ms 2 - 5 MB NPU Use Export Script
Yolo-X w8a8 SA8775P ADP Qualcomm® SA8775P TFLITE 3.265 ms 0 - 29 MB NPU Yolo-X.tflite
Yolo-X w8a8 SA8775P ADP Qualcomm® SA8775P QNN 2.642 ms 1 - 13 MB NPU Use Export Script
Yolo-X w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile TFLITE 2.875 ms 0 - 34 MB NPU Yolo-X.tflite
Yolo-X w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile QNN 2.29 ms 1 - 13 MB NPU Use Export Script
Yolo-X w8a8 Samsung Galaxy S23 Snapdragon® 8 Gen 2 Mobile ONNX 9.706 ms 0 - 38 MB NPU Yolo-X.onnx
Yolo-X w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile TFLITE 1.876 ms 0 - 48 MB NPU Yolo-X.tflite
Yolo-X w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile QNN 1.538 ms 1 - 43 MB NPU Use Export Script
Yolo-X w8a8 Samsung Galaxy S24 Snapdragon® 8 Gen 3 Mobile ONNX 6.939 ms 1 - 99 MB NPU Yolo-X.onnx
Yolo-X w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile TFLITE 1.636 ms 0 - 30 MB NPU Yolo-X.tflite
Yolo-X w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile QNN 1.296 ms 1 - 38 MB NPU Use Export Script
Yolo-X w8a8 Snapdragon 8 Elite QRD Snapdragon® 8 Elite Mobile ONNX 7.911 ms 3 - 81 MB NPU Yolo-X.onnx
Yolo-X w8a8 Snapdragon X Elite CRD Snapdragon® X Elite QNN 2.594 ms 1 - 1 MB NPU Use Export Script
Yolo-X w8a8 Snapdragon X Elite CRD Snapdragon® X Elite ONNX 10.219 ms 9 - 9 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)   : 37.1                                 
Estimated peak memory usage (MB): [0, 35]                              
Total # Ops                     : 310                                  
Compute Unit(s)                 : npu (310 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 --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.yolox.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 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

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