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EfficientViT-b2-cls

Image Classification
PyTorch
LiteRT
ONNX
backbone
real_time
android
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@@ -36,35 +36,34 @@ 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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- | EfficientViT-b2-cls | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 7.632 ms | 0 - 243 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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- | EfficientViT-b2-cls | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 7.523 ms | 0 - 214 MB | FP16 | NPU | [EfficientViT-b2-cls.so](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.so) |
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- | EfficientViT-b2-cls | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.135 ms | 0 - 140 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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- | EfficientViT-b2-cls | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 5.224 ms | 0 - 37 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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- | EfficientViT-b2-cls | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.947 ms | 1 - 34 MB | FP16 | NPU | [EfficientViT-b2-cls.so](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.so) |
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- | EfficientViT-b2-cls | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.979 ms | 1 - 44 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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- | EfficientViT-b2-cls | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 4.397 ms | 0 - 38 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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- | EfficientViT-b2-cls | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 4.345 ms | 0 - 37 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-b2-cls | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.721 ms | 1 - 40 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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- | EfficientViT-b2-cls | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 7.621 ms | 0 - 243 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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- | EfficientViT-b2-cls | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 7.228 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-b2-cls | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 9.071 ms | 0 - 35 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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- | EfficientViT-b2-cls | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 8.507 ms | 1 - 37 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-b2-cls | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 7.758 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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- | EfficientViT-b2-cls | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.867 ms | 50 - 50 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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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[efficientvit_b2_cls]"
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  ```
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-
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  ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
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  Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
@@ -116,7 +115,7 @@ EfficientViT-b2-cls
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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  Estimated inference time (ms) : 7.6
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- Estimated peak memory usage (MB): [0, 243]
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  Total # Ops : 379
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  Compute Unit(s) : NPU (379 ops)
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  ```
@@ -143,7 +142,7 @@ from qai_hub_models.models.efficientvit_b2_cls 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()
@@ -235,7 +234,8 @@ 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 EfficientViT-b2-cls can be found [here](https://github.com/CVHub520/efficientvit/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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+ | EfficientViT-b2-cls | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 7.612 ms | 0 - 233 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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+ | EfficientViT-b2-cls | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 7.507 ms | 0 - 214 MB | FP16 | NPU | [EfficientViT-b2-cls.so](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.so) |
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+ | EfficientViT-b2-cls | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 7.24 ms | 0 - 140 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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+ | EfficientViT-b2-cls | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 5.228 ms | 0 - 32 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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+ | EfficientViT-b2-cls | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 4.951 ms | 1 - 37 MB | FP16 | NPU | [EfficientViT-b2-cls.so](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.so) |
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+ | EfficientViT-b2-cls | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.823 ms | 1 - 42 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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+ | EfficientViT-b2-cls | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 5.172 ms | 0 - 39 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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+ | EfficientViT-b2-cls | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 5.265 ms | 1 - 39 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-b2-cls | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.62 ms | 1 - 41 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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+ | EfficientViT-b2-cls | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 7.78 ms | 0 - 232 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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+ | EfficientViT-b2-cls | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 7.207 ms | 1 - 4 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-b2-cls | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 9.053 ms | 0 - 35 MB | FP16 | NPU | [EfficientViT-b2-cls.tflite](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.tflite) |
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+ | EfficientViT-b2-cls | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 8.485 ms | 1 - 37 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-b2-cls | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 7.673 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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+ | EfficientViT-b2-cls | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 7.877 ms | 51 - 51 MB | FP16 | NPU | [EfficientViT-b2-cls.onnx](https://huggingface.co/qualcomm/EfficientViT-b2-cls/blob/main/EfficientViT-b2-cls.onnx) |
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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[efficientvit-b2-cls]"
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  ```
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  ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device
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  Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your
 
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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  Estimated inference time (ms) : 7.6
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+ Estimated peak memory usage (MB): [0, 233]
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  Total # Ops : 379
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  Compute Unit(s) : NPU (379 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 EfficientViT-b2-cls can be found
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+ [here](https://github.com/CVHub520/efficientvit/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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