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ConvNext-Base

Image Classification
PyTorch
TF Lite
ONNX
android
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@@ -34,29 +34,29 @@ 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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- | ConvNext-Base | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 7.748 ms | 0 - 22 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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- | ConvNext-Base | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 8.472 ms | 0 - 25 MB | FP16 | NPU | [ConvNext-Base.so](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.so) |
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- | ConvNext-Base | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 37.333 ms | 0 - 295 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.onnx) |
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- | ConvNext-Base | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 5.802 ms | 0 - 60 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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- | ConvNext-Base | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 6.165 ms | 1 - 60 MB | FP16 | NPU | [ConvNext-Base.so](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.so) |
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- | ConvNext-Base | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 26.475 ms | 1 - 141 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.onnx) |
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- | ConvNext-Base | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 5.023 ms | 0 - 73 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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- | ConvNext-Base | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 5.511 ms | 0 - 74 MB | FP16 | NPU | Use Export Script |
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- | ConvNext-Base | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 23.22 ms | 1 - 154 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.onnx) |
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- | ConvNext-Base | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 7.761 ms | 0 - 25 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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- | ConvNext-Base | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 8.189 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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- | ConvNext-Base | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 20.119 ms | 0 - 53 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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- | ConvNext-Base | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 21.257 ms | 0 - 53 MB | FP16 | NPU | Use Export Script |
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- | ConvNext-Base | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 8.576 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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- | ConvNext-Base | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 41.04 ms | 176 - 176 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.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
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  ```
@@ -113,7 +113,7 @@ ConvNext-Base
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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  Estimated inference time (ms) : 7.7
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- Estimated peak memory usage (MB): [0, 22]
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  Total # Ops : 598
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  Compute Unit(s) : NPU (598 ops)
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  ```
@@ -140,7 +140,7 @@ from qai_hub_models.models.convnext_base 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()
@@ -232,7 +232,8 @@ 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 ConvNext-Base 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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+ | ConvNext-Base | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 7.743 ms | 0 - 27 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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+ | ConvNext-Base | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 8.489 ms | 1 - 22 MB | FP16 | NPU | [ConvNext-Base.so](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.so) |
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+ | ConvNext-Base | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 37.604 ms | 0 - 413 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.onnx) |
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+ | ConvNext-Base | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 5.804 ms | 0 - 61 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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+ | ConvNext-Base | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 6.163 ms | 1 - 61 MB | FP16 | NPU | [ConvNext-Base.so](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.so) |
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+ | ConvNext-Base | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 26.61 ms | 1 - 142 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.onnx) |
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+ | ConvNext-Base | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 4.334 ms | 0 - 73 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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+ | ConvNext-Base | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 4.688 ms | 1 - 74 MB | FP16 | NPU | Use Export Script |
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+ | ConvNext-Base | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 23.177 ms | 1 - 153 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.onnx) |
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+ | ConvNext-Base | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 7.77 ms | 0 - 22 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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+ | ConvNext-Base | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 8.227 ms | 1 - 3 MB | FP16 | NPU | Use Export Script |
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+ | ConvNext-Base | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 20.143 ms | 0 - 54 MB | FP16 | NPU | [ConvNext-Base.tflite](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.tflite) |
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+ | ConvNext-Base | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 21.227 ms | 0 - 52 MB | FP16 | NPU | Use Export Script |
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+ | ConvNext-Base | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 8.566 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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+ | ConvNext-Base | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 40.983 ms | 175 - 175 MB | FP16 | NPU | [ConvNext-Base.onnx](https://huggingface.co/qualcomm/ConvNext-Base/blob/main/ConvNext-Base.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
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  ```
 
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  Device : Samsung Galaxy S23 (13)
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  Runtime : TFLITE
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  Estimated inference time (ms) : 7.7
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+ Estimated peak memory usage (MB): [0, 27]
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  Total # Ops : 598
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  Compute Unit(s) : NPU (598 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 ConvNext-Base 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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