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DeepLabV3-ResNet50

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@@ -35,24 +35,24 @@ More details on model performance across various devices, can be found
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  | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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- | DeepLabV3-ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 331.647 ms | 2 - 206 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 211.881 ms | 23 - 54 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 221.287 ms | 22 - 42 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 293.032 ms | 0 - 194 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | SA7255P ADP | SA7255P | TFLITE | 1187.038 ms | 21 - 41 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 291.902 ms | 0 - 210 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | SA8295P ADP | SA8295P | TFLITE | 278.809 ms | 23 - 45 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 292.163 ms | 0 - 180 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | SA8775P ADP | SA8775P | TFLITE | 592.15 ms | 23 - 43 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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- | DeepLabV3-ResNet50 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 773.346 ms | 22 - 54 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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  ## Installation
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- This model can be installed as a Python package via pip.
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  ```bash
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  pip install qai-hub-models
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  ```
@@ -108,8 +108,8 @@ Profiling Results
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  DeepLabV3-ResNet50
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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- Estimated inference time (ms) : 331.6
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- Estimated peak memory usage (MB): [2, 206]
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  Total # Ops : 100
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  Compute Unit(s) : GPU (98 ops) CPU (2 ops)
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  ```
@@ -136,7 +136,7 @@ from qai_hub_models.models.deeplabv3_resnet50 import Model
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  torch_model = Model.from_pretrained()
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  # Device
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- device = hub.Device("Samsung Galaxy S23")
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  # Trace model
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  input_shape = torch_model.get_input_spec()
@@ -228,7 +228,8 @@ Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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  ## License
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- * The license for the original implementation of DeepLabV3-ResNet50 can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE).
 
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  * 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)
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  | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
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  |---|---|---|---|---|---|---|---|---|
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+ | DeepLabV3-ResNet50 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 292.941 ms | 0 - 181 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 214.81 ms | 22 - 51 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 222.393 ms | 22 - 42 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 293.817 ms | 0 - 200 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | SA7255P ADP | SA7255P | TFLITE | 2148.816 ms | 21 - 41 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 294.006 ms | 0 - 190 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | SA8295P ADP | SA8295P | TFLITE | 283.104 ms | 23 - 45 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 292.801 ms | 0 - 233 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | SA8775P ADP | SA8775P | TFLITE | 593.557 ms | 23 - 39 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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+ | DeepLabV3-ResNet50 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 749.786 ms | 22 - 53 MB | FP16 | GPU | [DeepLabV3-ResNet50.tflite](https://huggingface.co/qualcomm/DeepLabV3-ResNet50/blob/main/DeepLabV3-ResNet50.tflite) |
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  ## Installation
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+ Install the package via pip:
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  ```bash
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  pip install qai-hub-models
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  ```
 
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  DeepLabV3-ResNet50
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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+ Estimated inference time (ms) : 292.9
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+ Estimated peak memory usage (MB): [0, 181]
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  Total # Ops : 100
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  Compute Unit(s) : GPU (98 ops) CPU (2 ops)
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  ```
 
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  torch_model = Model.from_pretrained()
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  # Device
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+ device = hub.Device("Samsung Galaxy S24")
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  # Trace model
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  input_shape = torch_model.get_input_spec()
 
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  ## License
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+ * The license for the original implementation of DeepLabV3-ResNet50 can be found
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+ [here](https://github.com/pytorch/vision/blob/main/LICENSE).
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  * 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)
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