DDRNet23-Slim / README.md

v0.45.0

a34801f verified 4 days ago

19.5 kB

	---
	library_name: pytorch
	license: other
	tags:
	- real_time
	- android
	pipeline_tag: image-segmentation

	---

	![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/ddrnet23_slim/web-assets/model_demo.png)

	# 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](https://github.com/chenjun2hao/DDRNet.pytorch).


	This repository provides scripts to run DDRNet23-Slim on Qualcomm® devices.
	More details on model performance across various devices, can be found
	[here](https://aihub.qualcomm.com/models/ddrnet23_slim).



	### Model Details

	- Model Type: Model_use_case.semantic_segmentation
	- Model Stats:
	- Model checkpoint: DDRNet23s_imagenet.pth
	- Inference latency: RealTime
	- Input resolution: 2048x1024
	- Number of output classes: 19
	- Number of parameters: 6.13M
	- Model size (float): 21.7 MB
	- Model size (w8a8): 6.11 MB

	\| Model \| Precision \| Device \| Chipset \| Target Runtime \| Inference Time (ms) \| Peak Memory Range (MB) \| Primary Compute Unit \| Target Model
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| DDRNet23-Slim \| float \| QCS8275 (Proxy) \| Qualcomm® QCS8275 (Proxy) \| TFLITE \| 94.17 ms \| 2 - 175 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| QCS8275 (Proxy) \| Qualcomm® QCS8275 (Proxy) \| QNN_DLC \| 94.127 ms \| 24 - 192 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| QCS8450 (Proxy) \| Qualcomm® QCS8450 (Proxy) \| TFLITE \| 59.585 ms \| 2 - 265 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| QCS8450 (Proxy) \| Qualcomm® QCS8450 (Proxy) \| QNN_DLC \| 60.235 ms \| 24 - 276 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| TFLITE \| 30.497 ms \| 0 - 2 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| QNN_DLC \| 30.449 ms \| 24 - 26 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| ONNX \| 24.761 ms \| 24 - 41 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.onnx.zip) \|
	\| DDRNet23-Slim \| float \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| TFLITE \| 37.971 ms \| 2 - 175 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| QNN_DLC \| 171.425 ms \| 21 - 188 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| SA7255P ADP \| Qualcomm® SA7255P \| TFLITE \| 94.17 ms \| 2 - 175 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| SA7255P ADP \| Qualcomm® SA7255P \| QNN_DLC \| 94.127 ms \| 24 - 192 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| SA8295P ADP \| Qualcomm® SA8295P \| TFLITE \| 42.222 ms \| 2 - 184 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| SA8295P ADP \| Qualcomm® SA8295P \| QNN_DLC \| 42.185 ms \| 24 - 198 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| SA8775P ADP \| Qualcomm® SA8775P \| TFLITE \| 37.971 ms \| 2 - 175 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| SA8775P ADP \| Qualcomm® SA8775P \| QNN_DLC \| 171.425 ms \| 21 - 188 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| TFLITE \| 20.627 ms \| 1 - 260 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| QNN_DLC \| 20.99 ms \| 17 - 270 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| ONNX \| 16.159 ms \| 32 - 252 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.onnx.zip) \|
	\| DDRNet23-Slim \| float \| Samsung Galaxy S25 \| Snapdragon® 8 Elite For Galaxy Mobile \| TFLITE \| 15.271 ms \| 1 - 191 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| Samsung Galaxy S25 \| Snapdragon® 8 Elite For Galaxy Mobile \| QNN_DLC \| 15.226 ms \| 16 - 198 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| Samsung Galaxy S25 \| Snapdragon® 8 Elite For Galaxy Mobile \| ONNX \| 12.691 ms \| 5 - 146 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.onnx.zip) \|
	\| DDRNet23-Slim \| float \| Snapdragon 8 Elite Gen 5 QRD \| Snapdragon® 8 Elite Gen 5 Mobile \| TFLITE \| 11.059 ms \| 2 - 201 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.tflite) \|
	\| DDRNet23-Slim \| float \| Snapdragon 8 Elite Gen 5 QRD \| Snapdragon® 8 Elite Gen 5 Mobile \| QNN_DLC \| 10.994 ms \| 24 - 218 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| Snapdragon 8 Elite Gen 5 QRD \| Snapdragon® 8 Elite Gen 5 Mobile \| ONNX \| 8.955 ms \| 29 - 218 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.onnx.zip) \|
	\| DDRNet23-Slim \| float \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| QNN_DLC \| 31.403 ms \| 24 - 24 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.dlc) \|
	\| DDRNet23-Slim \| float \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| ONNX \| 24.375 ms \| 24 - 24 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| Dragonwing Q-6690 MTP \| Qualcomm® QCM6690 \| TFLITE \| 214.038 ms \| 10 - 198 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| Dragonwing Q-6690 MTP \| Qualcomm® QCM6690 \| ONNX \| 268.402 ms \| 201 - 218 MB \| CPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| Dragonwing RB3 Gen 2 Vision Kit \| Qualcomm® QCS6490 \| TFLITE \| 180.82 ms \| 9 - 77 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| Dragonwing RB3 Gen 2 Vision Kit \| Qualcomm® QCS6490 \| ONNX \| 299.07 ms \| 198 - 217 MB \| CPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8275 (Proxy) \| Qualcomm® QCS8275 (Proxy) \| TFLITE \| 128.224 ms \| 1 - 169 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8275 (Proxy) \| Qualcomm® QCS8275 (Proxy) \| QNN_DLC \| 141.159 ms \| 6 - 175 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8450 (Proxy) \| Qualcomm® QCS8450 (Proxy) \| TFLITE \| 72.963 ms \| 1 - 221 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8450 (Proxy) \| Qualcomm® QCS8450 (Proxy) \| QNN_DLC \| 78.492 ms \| 6 - 225 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| TFLITE \| 51.185 ms \| 1 - 3 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| QNN_DLC \| 73.592 ms \| 6 - 8 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| QCS8550 (Proxy) \| Qualcomm® QCS8550 (Proxy) \| ONNX \| 74.892 ms \| 81 - 92 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| TFLITE \| 51.871 ms \| 1 - 169 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| QCS9075 (Proxy) \| Qualcomm® QCS9075 (Proxy) \| QNN_DLC \| 74.508 ms \| 6 - 175 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| SA7255P ADP \| Qualcomm® SA7255P \| TFLITE \| 128.224 ms \| 1 - 169 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| SA7255P ADP \| Qualcomm® SA7255P \| QNN_DLC \| 141.159 ms \| 6 - 175 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| SA8295P ADP \| Qualcomm® SA8295P \| TFLITE \| 63.44 ms \| 1 - 172 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| SA8295P ADP \| Qualcomm® SA8295P \| QNN_DLC \| 82.789 ms \| 6 - 178 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| SA8775P ADP \| Qualcomm® SA8775P \| TFLITE \| 51.871 ms \| 1 - 169 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| SA8775P ADP \| Qualcomm® SA8775P \| QNN_DLC \| 74.508 ms \| 6 - 175 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| TFLITE \| 38.618 ms \| 0 - 219 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| QNN_DLC \| 55.142 ms \| 6 - 224 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| Samsung Galaxy S24 \| Snapdragon® 8 Gen 3 Mobile \| ONNX \| 57.72 ms \| 92 - 286 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| Samsung Galaxy S25 \| Snapdragon® 8 Elite For Galaxy Mobile \| TFLITE \| 64.995 ms \| 1 - 182 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| Samsung Galaxy S25 \| Snapdragon® 8 Elite For Galaxy Mobile \| QNN_DLC \| 40.451 ms \| 6 - 184 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| Samsung Galaxy S25 \| Snapdragon® 8 Elite For Galaxy Mobile \| ONNX \| 42.233 ms \| 84 - 227 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| Snapdragon 7 Gen 4 QRD \| Snapdragon® 7 Gen 4 Mobile \| TFLITE \| 82.361 ms \| 9 - 178 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| Snapdragon 7 Gen 4 QRD \| Snapdragon® 7 Gen 4 Mobile \| ONNX \| 249.96 ms \| 207 - 224 MB \| CPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| Snapdragon 8 Elite Gen 5 QRD \| Snapdragon® 8 Elite Gen 5 Mobile \| TFLITE \| 40.142 ms \| 3 - 196 MB \| NPU \| [DDRNet23-Slim.tflite](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.tflite) \|
	\| DDRNet23-Slim \| w8a8 \| Snapdragon 8 Elite Gen 5 QRD \| Snapdragon® 8 Elite Gen 5 Mobile \| QNN_DLC \| 47.001 ms \| 6 - 201 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|
	\| DDRNet23-Slim \| w8a8 \| Snapdragon 8 Elite Gen 5 QRD \| Snapdragon® 8 Elite Gen 5 Mobile \| ONNX \| 41.735 ms \| 92 - 235 MB \| NPU \| [DDRNet23-Slim.onnx.zip](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.onnx.zip) \|
	\| DDRNet23-Slim \| w8a8 \| Snapdragon X Elite CRD \| Snapdragon® X Elite \| QNN_DLC \| 76.973 ms \| 6 - 6 MB \| NPU \| [DDRNet23-Slim.dlc](https://huggingface.co/qualcomm/DDRNet23-Slim/blob/main/DDRNet23-Slim_w8a8.dlc) \|




	## Installation


	Install the package via pip:
	```bash
	pip install qai-hub-models
	```


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

	Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.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://workbench.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.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.

	```bash
	python -m qai_hub_models.models.ddrnet23_slim.export
	```



	## How does this work?

	This [export script](https://aihub.qualcomm.com/models/ddrnet23_slim/qai_hub_models/models/DDRNet23-Slim/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.ddrnet23_slim 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.
	```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 Workbench. [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.ddrnet23_slim.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.ddrnet23_slim.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 DDRNet23-Slim's performance across various devices [here](https://aihub.qualcomm.com/models/ddrnet23_slim).
	Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)


	## License
	* The license for the original implementation of DDRNet23-Slim can be found
	[here](https://github.com/chenjun2hao/DDRNet.pytorch/blob/main/LICENSE).



	## References
	* [Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes](https://arxiv.org/abs/2101.06085)
	* [Source Model Implementation](https://github.com/chenjun2hao/DDRNet.pytorch)



	## 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).