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DeepLabV3-ResNet50

Image Segmentation
PyTorch
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v0.30.5

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See https://github.com/quic/ai-hub-models/releases/v0.30.5 for changelog.

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  1. README.md +16 -16
README.md CHANGED
@@ -35,18 +35,18 @@ More details on model performance across various devices, can be found
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  | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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- | DeepLabV3-ResNet50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 1185.986 ms | 6 - 23 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 786.162 ms | 21 - 51 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 304.269 ms | 3 - 201 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 718.986 ms | 23 - 39 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 1185.986 ms | 6 - 23 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 293.789 ms | 0 - 210 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 266.482 ms | 23 - 44 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 292.485 ms | 2 - 190 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 718.986 ms | 23 - 39 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 294.648 ms | 2 - 210 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 264.494 ms | 21 - 49 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 187.222 ms | 23 - 44 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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@@ -110,8 +110,8 @@ Profiling Results
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  DeepLabV3-ResNet50
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  Device : cs_8275 (ANDROID 14)
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  Runtime : TFLITE
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- Estimated inference time (ms) : 1186.0
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- Estimated peak memory usage (MB): [6, 23]
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  Total # Ops : 100
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  Compute Unit(s) : npu (0 ops) gpu (98 ops) cpu (2 ops)
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  ```
@@ -200,13 +200,13 @@ AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup).
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  You can also run the demo on-device.
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  ```bash
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- python -m qai_hub_models.models.deeplabv3_resnet50.demo --on-device
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  ```
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  **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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  environment, please add the following to your cell (instead of the above).
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  ```
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- %run -m qai_hub_models.models.deeplabv3_resnet50.demo -- --on-device
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  ```
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  | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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+ | DeepLabV3-ResNet50 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 957.543 ms | 23 - 39 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 755.742 ms | 22 - 54 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 607.841 ms | 0 - 234 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 593.018 ms | 23 - 43 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 957.543 ms | 23 - 39 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 381.486 ms | 0 - 204 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 287.319 ms | 23 - 45 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 297.843 ms | 0 - 209 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 593.018 ms | 23 - 43 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 292.35 ms | 2 - 191 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 475.897 ms | 22 - 50 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 186.432 ms | 22 - 41 MB | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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  DeepLabV3-ResNet50
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  Device : cs_8275 (ANDROID 14)
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  Runtime : TFLITE
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+ Estimated inference time (ms) : 957.5
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+ Estimated peak memory usage (MB): [23, 39]
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  Total # Ops : 100
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  Compute Unit(s) : npu (0 ops) gpu (98 ops) cpu (2 ops)
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  ```
 
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  You can also run the demo on-device.
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  ```bash
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+ python -m qai_hub_models.models.deeplabv3_resnet50.demo --eval-mode on-device
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  ```
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  **NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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  environment, please add the following to your cell (instead of the above).
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  ```
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+ %run -m qai_hub_models.models.deeplabv3_resnet50.demo -- --eval-mode on-device
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  ```
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