v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- DeepLabXception_float.dlc +0 -3
- DeepLabXception_float.onnx.zip +0 -3
- DeepLabXception_float.tflite +0 -3
- DeepLabXception_w8a8.dlc +0 -3
- DeepLabXception_w8a8.onnx.zip +0 -3
- DeepLabXception_w8a8.tflite +0 -3
- README.md +113 -262
- tool-versions.yaml +0 -4
DeepLabXception_float.dlc
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:c76e3dad642c1c21b99f0e082350e58518c7c73b2b80cb50d7c0b7aa486086dc
|
| 3 |
-
size 164174044
|
|
|
|
|
|
|
|
|
|
|
|
DeepLabXception_float.onnx.zip
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:18c475d6461d835112fc6c065ee5d2670d1350b51b555accb1bcbd8d09512464
|
| 3 |
-
size 153177400
|
|
|
|
|
|
|
|
|
|
|
|
DeepLabXception_float.tflite
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:5f8abf9ca793c1d0fa2ccf25df258469de2ca05a1808cfc220ec2240b60b2cf9
|
| 3 |
-
size 163888820
|
|
|
|
|
|
|
|
|
|
|
|
DeepLabXception_w8a8.dlc
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:16d0f6d766802e472f089f7c1243fb8616125bcd68cf2fd64154836fef17e17d
|
| 3 |
-
size 47312884
|
|
|
|
|
|
|
|
|
|
|
|
DeepLabXception_w8a8.onnx.zip
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:5071218ead6b41651fae83504f91eded31ff5587b8e87d6dfe015061d498ab7d
|
| 3 |
-
size 77181345
|
|
|
|
|
|
|
|
|
|
|
|
DeepLabXception_w8a8.tflite
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:8db1537a75bd37aeeb66474094d8c72fb17d63e06e6c3f47cb700f3639491c70
|
| 3 |
-
size 43792616
|
|
|
|
|
|
|
|
|
|
|
|
README.md
CHANGED
|
@@ -10,280 +10,131 @@ pipeline_tag: image-segmentation
|
|
| 10 |
|
| 11 |
|
| 12 |
|
| 13 |
-
# DeepLabXception: Optimized for
|
| 14 |
-
## Deep Convolutional Neural Network model for semantic segmentation
|
| 15 |
-
|
| 16 |
|
| 17 |
DeepLabXception is a semantic segmentation model supporting multiple backbones like ResNet-101 and Xception, with flexible dataset compatibility including COCO, VOC, and Cityscapes.
|
| 18 |
|
| 19 |
-
This
|
| 20 |
-
|
| 21 |
-
|
| 22 |
-
|
| 23 |
-
|
| 24 |
-
|
| 25 |
-
|
| 26 |
-
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
|
| 34 |
-
|
| 35 |
-
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
|
|
| 39 |
-
|
| 40 |
-
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
|
| 44 |
-
|
| 45 |
-
|
| 46 |
-
|
| 47 |
-
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
|
| 54 |
-
|
| 55 |
-
|
| 56 |
-
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
|
|
| 67 |
-
|
|
| 68 |
-
| DeepLabXception |
|
| 69 |
-
| DeepLabXception |
|
| 70 |
-
| DeepLabXception |
|
| 71 |
-
| DeepLabXception |
|
| 72 |
-
| DeepLabXception |
|
| 73 |
-
| DeepLabXception |
|
| 74 |
-
| DeepLabXception |
|
| 75 |
-
| DeepLabXception |
|
| 76 |
-
| DeepLabXception |
|
| 77 |
-
| DeepLabXception |
|
| 78 |
-
| DeepLabXception |
|
| 79 |
-
| DeepLabXception |
|
| 80 |
-
| DeepLabXception |
|
| 81 |
-
| DeepLabXception |
|
| 82 |
-
| DeepLabXception |
|
| 83 |
-
| DeepLabXception |
|
| 84 |
-
| DeepLabXception |
|
| 85 |
-
| DeepLabXception |
|
| 86 |
-
| DeepLabXception |
|
| 87 |
-
| DeepLabXception |
|
| 88 |
-
| DeepLabXception |
|
| 89 |
-
| DeepLabXception |
|
| 90 |
-
| DeepLabXception |
|
| 91 |
-
| DeepLabXception |
|
| 92 |
-
| DeepLabXception |
|
| 93 |
-
| DeepLabXception |
|
| 94 |
-
| DeepLabXception |
|
| 95 |
-
| DeepLabXception |
|
| 96 |
-
| DeepLabXception |
|
| 97 |
-
| DeepLabXception |
|
| 98 |
-
| DeepLabXception |
|
| 99 |
-
| DeepLabXception |
|
| 100 |
-
| DeepLabXception |
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
|
| 104 |
-
|
| 105 |
-
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
|
| 109 |
-
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
|
| 113 |
-
|
| 114 |
-
|
| 115 |
-
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
|
| 119 |
-
|
| 120 |
-
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
|
| 124 |
-
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
|
| 128 |
-
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
weights and runs this model on a sample input.
|
| 132 |
-
|
| 133 |
-
```bash
|
| 134 |
-
python -m qai_hub_models.models.deeplab_xception.demo
|
| 135 |
-
```
|
| 136 |
-
|
| 137 |
-
The above demo runs a reference implementation of pre-processing, model
|
| 138 |
-
inference, and post processing.
|
| 139 |
-
|
| 140 |
-
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
|
| 141 |
-
environment, please add the following to your cell (instead of the above).
|
| 142 |
-
```
|
| 143 |
-
%run -m qai_hub_models.models.deeplab_xception.demo
|
| 144 |
-
```
|
| 145 |
-
|
| 146 |
-
|
| 147 |
-
### Run model on a cloud-hosted device
|
| 148 |
-
|
| 149 |
-
In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
|
| 150 |
-
device. This script does the following:
|
| 151 |
-
* Performance check on-device on a cloud-hosted device
|
| 152 |
-
* Downloads compiled assets that can be deployed on-device for Android.
|
| 153 |
-
* Accuracy check between PyTorch and on-device outputs.
|
| 154 |
-
|
| 155 |
-
```bash
|
| 156 |
-
python -m qai_hub_models.models.deeplab_xception.export
|
| 157 |
-
```
|
| 158 |
-
|
| 159 |
-
|
| 160 |
-
|
| 161 |
-
## How does this work?
|
| 162 |
-
|
| 163 |
-
This [export script](https://aihub.qualcomm.com/models/deeplab_xception/qai_hub_models/models/DeepLabXception/export.py)
|
| 164 |
-
leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
|
| 165 |
-
on-device. Lets go through each step below in detail:
|
| 166 |
-
|
| 167 |
-
Step 1: **Compile model for on-device deployment**
|
| 168 |
-
|
| 169 |
-
To compile a PyTorch model for on-device deployment, we first trace the model
|
| 170 |
-
in memory using the `jit.trace` and then call the `submit_compile_job` API.
|
| 171 |
-
|
| 172 |
-
```python
|
| 173 |
-
import torch
|
| 174 |
-
|
| 175 |
-
import qai_hub as hub
|
| 176 |
-
from qai_hub_models.models.deeplab_xception import Model
|
| 177 |
-
|
| 178 |
-
# Load the model
|
| 179 |
-
torch_model = Model.from_pretrained()
|
| 180 |
-
|
| 181 |
-
# Device
|
| 182 |
-
device = hub.Device("Samsung Galaxy S25")
|
| 183 |
-
|
| 184 |
-
# Trace model
|
| 185 |
-
input_shape = torch_model.get_input_spec()
|
| 186 |
-
sample_inputs = torch_model.sample_inputs()
|
| 187 |
-
|
| 188 |
-
pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
|
| 189 |
-
|
| 190 |
-
# Compile model on a specific device
|
| 191 |
-
compile_job = hub.submit_compile_job(
|
| 192 |
-
model=pt_model,
|
| 193 |
-
device=device,
|
| 194 |
-
input_specs=torch_model.get_input_spec(),
|
| 195 |
-
)
|
| 196 |
-
|
| 197 |
-
# Get target model to run on-device
|
| 198 |
-
target_model = compile_job.get_target_model()
|
| 199 |
-
|
| 200 |
-
```
|
| 201 |
-
|
| 202 |
-
|
| 203 |
-
Step 2: **Performance profiling on cloud-hosted device**
|
| 204 |
-
|
| 205 |
-
After compiling models from step 1. Models can be profiled model on-device using the
|
| 206 |
-
`target_model`. Note that this scripts runs the model on a device automatically
|
| 207 |
-
provisioned in the cloud. Once the job is submitted, you can navigate to a
|
| 208 |
-
provided job URL to view a variety of on-device performance metrics.
|
| 209 |
-
```python
|
| 210 |
-
profile_job = hub.submit_profile_job(
|
| 211 |
-
model=target_model,
|
| 212 |
-
device=device,
|
| 213 |
-
)
|
| 214 |
-
|
| 215 |
-
```
|
| 216 |
-
|
| 217 |
-
Step 3: **Verify on-device accuracy**
|
| 218 |
-
|
| 219 |
-
To verify the accuracy of the model on-device, you can run on-device inference
|
| 220 |
-
on sample input data on the same cloud hosted device.
|
| 221 |
-
```python
|
| 222 |
-
input_data = torch_model.sample_inputs()
|
| 223 |
-
inference_job = hub.submit_inference_job(
|
| 224 |
-
model=target_model,
|
| 225 |
-
device=device,
|
| 226 |
-
inputs=input_data,
|
| 227 |
-
)
|
| 228 |
-
on_device_output = inference_job.download_output_data()
|
| 229 |
-
|
| 230 |
-
```
|
| 231 |
-
With the output of the model, you can compute like PSNR, relative errors or
|
| 232 |
-
spot check the output with expected output.
|
| 233 |
-
|
| 234 |
-
**Note**: This on-device profiling and inference requires access to Qualcomm®
|
| 235 |
-
AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
|
| 236 |
-
|
| 237 |
-
|
| 238 |
-
|
| 239 |
-
## Run demo on a cloud-hosted device
|
| 240 |
-
|
| 241 |
-
You can also run the demo on-device.
|
| 242 |
-
|
| 243 |
-
```bash
|
| 244 |
-
python -m qai_hub_models.models.deeplab_xception.demo --eval-mode on-device
|
| 245 |
-
```
|
| 246 |
-
|
| 247 |
-
**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
|
| 248 |
-
environment, please add the following to your cell (instead of the above).
|
| 249 |
-
```
|
| 250 |
-
%run -m qai_hub_models.models.deeplab_xception.demo -- --eval-mode on-device
|
| 251 |
-
```
|
| 252 |
-
|
| 253 |
-
|
| 254 |
-
## Deploying compiled model to Android
|
| 255 |
-
|
| 256 |
-
|
| 257 |
-
The models can be deployed using multiple runtimes:
|
| 258 |
-
- TensorFlow Lite (`.tflite` export): [This
|
| 259 |
-
tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
|
| 260 |
-
guide to deploy the .tflite model in an Android application.
|
| 261 |
-
|
| 262 |
-
|
| 263 |
-
- QNN (`.so` export ): This [sample
|
| 264 |
-
app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
|
| 265 |
-
provides instructions on how to use the `.so` shared library in an Android application.
|
| 266 |
-
|
| 267 |
-
|
| 268 |
-
## View on Qualcomm® AI Hub
|
| 269 |
-
Get more details on DeepLabXception's performance across various devices [here](https://aihub.qualcomm.com/models/deeplab_xception).
|
| 270 |
-
Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
|
| 271 |
-
|
| 272 |
|
| 273 |
## License
|
| 274 |
* The license for the original implementation of DeepLabXception can be found
|
| 275 |
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
|
| 276 |
|
| 277 |
-
|
| 278 |
-
|
| 279 |
## References
|
| 280 |
* [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
|
| 281 |
* [Source Model Implementation](https://github.com/LikeLy-Journey/SegmenTron)
|
| 282 |
|
| 283 |
-
|
| 284 |
-
|
| 285 |
## Community
|
| 286 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 287 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
| 288 |
-
|
| 289 |
-
|
|
|
|
| 10 |
|
| 11 |
|
| 12 |
|
| 13 |
+
# DeepLabXception: Optimized for Qualcomm Devices
|
|
|
|
|
|
|
| 14 |
|
| 15 |
DeepLabXception is a semantic segmentation model supporting multiple backbones like ResNet-101 and Xception, with flexible dataset compatibility including COCO, VOC, and Cityscapes.
|
| 16 |
|
| 17 |
+
This is based on the implementation of DeepLabXception found [here](https://github.com/LikeLy-Journey/SegmenTron).
|
| 18 |
+
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/deeplab_xception) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
|
| 19 |
+
|
| 20 |
+
Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
|
| 21 |
+
|
| 22 |
+
## Getting Started
|
| 23 |
+
There are two ways to deploy this model on your device:
|
| 24 |
+
|
| 25 |
+
### Option 1: Download Pre-Exported Models
|
| 26 |
+
|
| 27 |
+
Below are pre-exported model assets ready for deployment.
|
| 28 |
+
|
| 29 |
+
| Runtime | Precision | Chipset | SDK Versions | Download |
|
| 30 |
+
|---|---|---|---|---|
|
| 31 |
+
| ONNX | float | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplab_xception/releases/v0.46.1/deeplab_xception-onnx-float.zip)
|
| 32 |
+
| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplab_xception/releases/v0.46.1/deeplab_xception-onnx-w8a8.zip)
|
| 33 |
+
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplab_xception/releases/v0.46.1/deeplab_xception-qnn_dlc-float.zip)
|
| 34 |
+
| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplab_xception/releases/v0.46.1/deeplab_xception-qnn_dlc-w8a8.zip)
|
| 35 |
+
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplab_xception/releases/v0.46.1/deeplab_xception-tflite-float.zip)
|
| 36 |
+
| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplab_xception/releases/v0.46.1/deeplab_xception-tflite-w8a8.zip)
|
| 37 |
+
|
| 38 |
+
For more device-specific assets and performance metrics, visit **[DeepLabXception on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/deeplab_xception)**.
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
### Option 2: Export with Custom Configurations
|
| 42 |
+
|
| 43 |
+
Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/deeplab_xception) Python library to compile and export the model with your own:
|
| 44 |
+
- Custom weights (e.g., fine-tuned checkpoints)
|
| 45 |
+
- Custom input shapes
|
| 46 |
+
- Target device and runtime configurations
|
| 47 |
+
|
| 48 |
+
This option is ideal if you need to customize the model beyond the default configuration provided here.
|
| 49 |
+
|
| 50 |
+
See our repository for [DeepLabXception on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/deeplab_xception) for usage instructions.
|
| 51 |
+
|
| 52 |
+
## Model Details
|
| 53 |
+
|
| 54 |
+
**Model Type:** Model_use_case.semantic_segmentation
|
| 55 |
+
|
| 56 |
+
**Model Stats:**
|
| 57 |
+
- Model checkpoint: COCO_WITH_VOC_LABELS_V1
|
| 58 |
+
- Input resolution: 480x520
|
| 59 |
+
- Number of output classes: 21
|
| 60 |
+
- Number of parameters: 41.26M
|
| 61 |
+
- Model size (float): 158 MB
|
| 62 |
+
|
| 63 |
+
## Performance Summary
|
| 64 |
+
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|
| 65 |
+
|---|---|---|---|---|---|---
|
| 66 |
+
| DeepLabXception | ONNX | float | Snapdragon® X Elite | 21.791 ms | 86 - 86 MB | NPU
|
| 67 |
+
| DeepLabXception | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 15.806 ms | 3 - 304 MB | NPU
|
| 68 |
+
| DeepLabXception | ONNX | float | Qualcomm® QCS8550 (Proxy) | 20.959 ms | 0 - 96 MB | NPU
|
| 69 |
+
| DeepLabXception | ONNX | float | Qualcomm® QCS9075 | 32.676 ms | 3 - 8 MB | NPU
|
| 70 |
+
| DeepLabXception | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 12.183 ms | 2 - 185 MB | NPU
|
| 71 |
+
| DeepLabXception | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.236 ms | 0 - 198 MB | NPU
|
| 72 |
+
| DeepLabXception | ONNX | w8a8 | Snapdragon® X Elite | 8.003 ms | 44 - 44 MB | NPU
|
| 73 |
+
| DeepLabXception | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 5.35 ms | 0 - 337 MB | NPU
|
| 74 |
+
| DeepLabXception | ONNX | w8a8 | Qualcomm® QCS6490 | 310.74 ms | 89 - 122 MB | CPU
|
| 75 |
+
| DeepLabXception | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 7.601 ms | 0 - 51 MB | NPU
|
| 76 |
+
| DeepLabXception | ONNX | w8a8 | Qualcomm® QCS9075 | 7.881 ms | 0 - 4 MB | NPU
|
| 77 |
+
| DeepLabXception | ONNX | w8a8 | Qualcomm® QCM6690 | 242.805 ms | 57 - 71 MB | CPU
|
| 78 |
+
| DeepLabXception | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 4.337 ms | 0 - 233 MB | NPU
|
| 79 |
+
| DeepLabXception | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 227.823 ms | 91 - 103 MB | CPU
|
| 80 |
+
| DeepLabXception | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 3.299 ms | 0 - 237 MB | NPU
|
| 81 |
+
| DeepLabXception | QNN_DLC | float | Snapdragon® X Elite | 22.787 ms | 3 - 3 MB | NPU
|
| 82 |
+
| DeepLabXception | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 16.092 ms | 0 - 366 MB | NPU
|
| 83 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 113.009 ms | 1 - 245 MB | NPU
|
| 84 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 22.013 ms | 3 - 6 MB | NPU
|
| 85 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® SA8775P | 30.552 ms | 2 - 246 MB | NPU
|
| 86 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® QCS9075 | 35.61 ms | 5 - 10 MB | NPU
|
| 87 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 46.253 ms | 0 - 361 MB | NPU
|
| 88 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® SA7255P | 113.009 ms | 1 - 245 MB | NPU
|
| 89 |
+
| DeepLabXception | QNN_DLC | float | Qualcomm® SA8295P | 38.823 ms | 0 - 242 MB | NPU
|
| 90 |
+
| DeepLabXception | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 12.132 ms | 0 - 249 MB | NPU
|
| 91 |
+
| DeepLabXception | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.141 ms | 3 - 258 MB | NPU
|
| 92 |
+
| DeepLabXception | QNN_DLC | w8a8 | Snapdragon® X Elite | 8.561 ms | 1 - 1 MB | NPU
|
| 93 |
+
| DeepLabXception | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 5.614 ms | 1 - 373 MB | NPU
|
| 94 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 33.905 ms | 1 - 3 MB | NPU
|
| 95 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 20.273 ms | 1 - 283 MB | NPU
|
| 96 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 7.903 ms | 1 - 3 MB | NPU
|
| 97 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® SA8775P | 8.061 ms | 1 - 285 MB | NPU
|
| 98 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 8.573 ms | 1 - 3 MB | NPU
|
| 99 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 121.023 ms | 1 - 307 MB | NPU
|
| 100 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 14.214 ms | 1 - 367 MB | NPU
|
| 101 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® SA7255P | 20.273 ms | 1 - 283 MB | NPU
|
| 102 |
+
| DeepLabXception | QNN_DLC | w8a8 | Qualcomm® SA8295P | 12.638 ms | 1 - 275 MB | NPU
|
| 103 |
+
| DeepLabXception | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 4.411 ms | 1 - 273 MB | NPU
|
| 104 |
+
| DeepLabXception | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 13.07 ms | 1 - 277 MB | NPU
|
| 105 |
+
| DeepLabXception | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 3.297 ms | 1 - 274 MB | NPU
|
| 106 |
+
| DeepLabXception | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 16.101 ms | 0 - 483 MB | NPU
|
| 107 |
+
| DeepLabXception | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 113.095 ms | 0 - 358 MB | NPU
|
| 108 |
+
| DeepLabXception | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 21.319 ms | 0 - 3 MB | NPU
|
| 109 |
+
| DeepLabXception | TFLITE | float | Qualcomm® SA8775P | 30.518 ms | 0 - 358 MB | NPU
|
| 110 |
+
| DeepLabXception | TFLITE | float | Qualcomm® QCS9075 | 35.068 ms | 0 - 93 MB | NPU
|
| 111 |
+
| DeepLabXception | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 46.083 ms | 0 - 471 MB | NPU
|
| 112 |
+
| DeepLabXception | TFLITE | float | Qualcomm® SA7255P | 113.095 ms | 0 - 358 MB | NPU
|
| 113 |
+
| DeepLabXception | TFLITE | float | Qualcomm® SA8295P | 38.54 ms | 0 - 349 MB | NPU
|
| 114 |
+
| DeepLabXception | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 12.23 ms | 0 - 369 MB | NPU
|
| 115 |
+
| DeepLabXception | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.127 ms | 0 - 370 MB | NPU
|
| 116 |
+
| DeepLabXception | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 5.024 ms | 0 - 373 MB | NPU
|
| 117 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® QCS6490 | 31.912 ms | 0 - 58 MB | NPU
|
| 118 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 19.046 ms | 0 - 282 MB | NPU
|
| 119 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 6.974 ms | 0 - 3 MB | NPU
|
| 120 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® SA8775P | 7.236 ms | 0 - 284 MB | NPU
|
| 121 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® QCS9075 | 7.448 ms | 0 - 47 MB | NPU
|
| 122 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® QCM6690 | 122.599 ms | 0 - 294 MB | NPU
|
| 123 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 12.47 ms | 0 - 362 MB | NPU
|
| 124 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® SA7255P | 19.046 ms | 0 - 282 MB | NPU
|
| 125 |
+
| DeepLabXception | TFLITE | w8a8 | Qualcomm® SA8295P | 11.577 ms | 0 - 275 MB | NPU
|
| 126 |
+
| DeepLabXception | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 3.939 ms | 0 - 274 MB | NPU
|
| 127 |
+
| DeepLabXception | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 11.535 ms | 0 - 266 MB | NPU
|
| 128 |
+
| DeepLabXception | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 2.911 ms | 0 - 269 MB | NPU
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 129 |
|
| 130 |
## License
|
| 131 |
* The license for the original implementation of DeepLabXception can be found
|
| 132 |
[here](https://github.com/pytorch/vision/blob/main/LICENSE).
|
| 133 |
|
|
|
|
|
|
|
| 134 |
## References
|
| 135 |
* [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1802.02611)
|
| 136 |
* [Source Model Implementation](https://github.com/LikeLy-Journey/SegmenTron)
|
| 137 |
|
|
|
|
|
|
|
| 138 |
## Community
|
| 139 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 140 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
|
|
|
|
|
tool-versions.yaml
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
tool_versions:
|
| 2 |
-
onnx:
|
| 3 |
-
qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|