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qualcomm / DDRNet23-Slim
Model's Last Updated: October 07 2026
image-segmentation
Introduction of DDRNet23-Slim
Model Details of DDRNet23-Slim
DDRNet23-Slim: Optimized for Mobile Deployment
Segment images or video by class in real-time on device
DDRNet23Slim is a machine learning model that segments an image into semantic classes, specifically designed for road-based scenes. It is designed for the application of self-driving cars.
This model is an implementation of DDRNet23-Slim found here . This repository provides scripts to run DDRNet23-Slim on Qualcomm® devices. More details on model performance across various devices, can be found here .
Model Details
- Model Type: Semantic segmentation
-
Model Stats:
- Model checkpoint: DDRNet23s_imagenet.pth
- Inference latency: RealTime
- Input resolution: 2048x1024
- Number of parameters: 5.69M
- Model size: 21.7 MB
- Number of output classes: 19
| Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|
| Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 5.108 ms | 1 - 4 MB | FP16 | NPU | DDRNet23-Slim.tflite |
Installation
This model can be installed as a Python package via pip.
pip install qai-hub-models
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.ddrnet23_slim.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.ddrnet23_slim.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.ddrnet23_slim.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.ddrnet23_slim import Model
# Load the model
torch_model = Model.from_pretrained()
# Device
device = hub.Device("Samsung Galaxy S23")
# 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.ddrnet23_slim.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.ddrnet23_slim.demo -- --on-device
Deploying compiled model to Android
The models can be deployed using multiple runtimes:
-
TensorFlow Lite (
.tfliteexport): This tutorial provides a guide to deploy the .tflite model in an Android application. -
QNN (
.soexport ): This sample app provides instructions on how to use the.soshared library in an Android application.
View on Qualcomm® AI Hub
Get more details on DDRNet23-Slim's performance across various devices here . Explore all available models on Qualcomm® AI Hub
License
- The license for the original implementation of DDRNet23-Slim can be found here .
- The license for the compiled assets for on-device deployment can be found here
References
- Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes
- Source Model Implementation
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 .
Runs of qualcomm DDRNet23-Slim on huggingface.co
70
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More Information About DDRNet23-Slim huggingface.co Model
Url of DDRNet23-Slim
DDRNet23-Slim huggingface.co Url
Provider of DDRNet23-Slim huggingface.co
Other API from qualcomm
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