README.md

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

---

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

# DeepLabXception: Optimized for Mobile Deployment
## Deep Convolutional Neural Network model for semantic segmentation


DeepLabXception is a semantic segmentation model supporting multiple backbones like ResNet-101 and Xception, with flexible dataset compatibility including COCO, VOC, and Cityscapes.

This model is an implementation of DeepLabXception found [here](https://github.com/LikeLy-Journey/SegmenTron).


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



### Model Details

- **Model Type:** Model_use_case.semantic_segmentation
- **Model Stats:**
  - Model checkpoint: COCO_WITH_VOC_LABELS_V1
  - Input resolution: 480x520
  - Number of output classes: 21
  - Number of parameters: 41.26M
  - Model size (float): 158 MB

| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
|---|---|---|---|---|---|---|---|---|
| DeepLabXception | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 120.892 ms | 0 - 171 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 112.829 ms | 0 - 76 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 40.778 ms | 0 - 172 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 52.14 ms | 0 - 79 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 24.657 ms | 0 - 26 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 21.564 ms | 3 - 33 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 34.888 ms | 0 - 170 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 30.527 ms | 0 - 76 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 120.892 ms | 0 - 171 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 112.829 ms | 0 - 76 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 24.881 ms | 0 - 23 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 21.888 ms | 3 - 32 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 45.182 ms | 0 - 167 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 39.099 ms | 0 - 81 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 24.559 ms | 0 - 26 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 21.801 ms | 2 - 32 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 34.888 ms | 0 - 170 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 30.527 ms | 0 - 76 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 24.776 ms | 0 - 22 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 21.808 ms | 3 - 33 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 21.94 ms | 0 - 115 MB | NPU | [DeepLabXception.onnx.zip](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.onnx.zip) |
| DeepLabXception | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 18.293 ms | 0 - 196 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 15.963 ms | 3 - 110 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 16.518 ms | 3 - 97 MB | NPU | [DeepLabXception.onnx.zip](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.onnx.zip) |
| DeepLabXception | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 16.852 ms | 0 - 173 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.tflite) |
| DeepLabXception | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 14.019 ms | 3 - 85 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 14.651 ms | 3 - 70 MB | NPU | [DeepLabXception.onnx.zip](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.onnx.zip) |
| DeepLabXception | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 22.614 ms | 189 - 189 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.dlc) |
| DeepLabXception | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 23.402 ms | 85 - 85 MB | NPU | [DeepLabXception.onnx.zip](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception.onnx.zip) |
| DeepLabXception | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 20.477 ms | 0 - 114 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 20.663 ms | 1 - 142 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 9.723 ms | 0 - 129 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 15.459 ms | 1 - 146 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 7.536 ms | 0 - 21 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 8.032 ms | 1 - 31 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 7.983 ms | 0 - 112 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 8.241 ms | 1 - 138 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | TFLITE | 37.192 ms | 0 - 119 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | RB3 Gen 2 (Proxy) | Qualcomm® QCS6490 (Proxy) | QNN_DLC | 55.477 ms | 1 - 136 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | TFLITE | 222.004 ms | 21 - 37 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 20.477 ms | 0 - 114 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 20.663 ms | 1 - 142 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 7.568 ms | 0 - 18 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 8.047 ms | 0 - 27 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 12.857 ms | 0 - 113 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 12.906 ms | 1 - 131 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 7.536 ms | 0 - 22 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 8.041 ms | 1 - 26 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 7.983 ms | 0 - 112 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 8.241 ms | 1 - 138 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 7.525 ms | 0 - 22 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN_DLC | 8.031 ms | 0 - 31 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 5.41 ms | 0 - 139 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 5.673 ms | 0 - 161 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 5.039 ms | 0 - 115 MB | NPU | [DeepLabXception.tflite](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.tflite) |
| DeepLabXception | w8a8 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN_DLC | 4.3 ms | 1 - 128 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |
| DeepLabXception | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 8.708 ms | 131 - 131 MB | NPU | [DeepLabXception.dlc](https://huggingface.co/qualcomm/DeepLabXception/blob/main/DeepLabXception_w8a8.dlc) |




## Installation


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


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

Sign-in to [Qualcomm® AI Hub](https://app.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://app.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.deeplab_xception.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.deeplab_xception.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.deeplab_xception.export
```



## How does this work?

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


## License
* The license for the original implementation of DeepLabXception can be found
  [here](https://github.com/pytorch/vision/blob/main/LICENSE).
* The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf)



## References
* [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
* [Source Model Implementation](https://github.com/LikeLy-Journey/SegmenTron)



## 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).