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DenseNet-121

Image Classification
PyTorch
TF Lite
ONNX
backbone
android
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  Densenet is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
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- This model is an implementation of DenseNet-121 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py).
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  This repository provides scripts to run DenseNet-121 on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/densenet121).
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  - Number of parameters: 7.97M
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  - Model size: 30.5 MB
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- | 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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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | TFLite | 1.93 ms | 0 - 236 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite)
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- | Samsung Galaxy S23 Ultra (Android 13) | Snapdragon® 8 Gen 2 | QNN Model Library | 1.994 ms | 0 - 5 MB | FP16 | NPU | [DenseNet-121.so](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.so)
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-
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-
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.densenet121.export
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  ```
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-
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  ```
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- Profile Job summary of DenseNet-121
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- --------------------------------------------------
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- Device: Snapdragon X Elite CRD (11)
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- Estimated Inference Time: 2.01 ms
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- Estimated Peak Memory Range: 0.57-0.57 MB
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- Compute Units: NPU (372) | Total (372)
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-
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-
 
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  ```
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  Get more details on DenseNet-121's performance across various devices [here](https://aihub.qualcomm.com/models/densenet121).
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  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 DenseNet-121 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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  ## References
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  * [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
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  * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py)
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
 
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  Densenet is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases.
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+ This model is an implementation of DenseNet-121 found [here]({source_repo}).
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  This repository provides scripts to run DenseNet-121 on Qualcomm® devices.
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  More details on model performance across various devices, can be found
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  [here](https://aihub.qualcomm.com/models/densenet121).
 
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  - Number of parameters: 7.97M
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  - Model size: 30.5 MB
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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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+ | DenseNet-121 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 1.922 ms | 0 - 6 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 1.99 ms | 1 - 5 MB | FP16 | NPU | [DenseNet-121.so](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.so) |
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+ | DenseNet-121 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 1.872 ms | 0 - 17 MB | FP16 | NPU | [DenseNet-121.onnx](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.onnx) |
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+ | DenseNet-121 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 1.425 ms | 0 - 100 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.474 ms | 0 - 19 MB | FP16 | NPU | [DenseNet-121.so](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.so) |
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+ | DenseNet-121 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 1.458 ms | 0 - 106 MB | FP16 | NPU | [DenseNet-121.onnx](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.onnx) |
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+ | DenseNet-121 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 1.92 ms | 0 - 26 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 1.788 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 1.928 ms | 0 - 1 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | SA8255 (Proxy) | SA8255P Proxy | QNN | 1.799 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | SA8775 (Proxy) | SA8775P Proxy | TFLITE | 1.927 ms | 0 - 2 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | SA8775 (Proxy) | SA8775P Proxy | QNN | 1.791 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 1.922 ms | 0 - 213 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | SA8650 (Proxy) | SA8650P Proxy | QNN | 1.803 ms | 1 - 2 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 2.617 ms | 0 - 102 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 2.723 ms | 0 - 23 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 1.004 ms | 0 - 26 MB | FP16 | NPU | [DenseNet-121.tflite](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.tflite) |
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+ | DenseNet-121 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 1.293 ms | 0 - 18 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 1.297 ms | 0 - 31 MB | FP16 | NPU | [DenseNet-121.onnx](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.onnx) |
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+ | DenseNet-121 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 2.019 ms | 1 - 1 MB | FP16 | NPU | Use Export Script |
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+ | DenseNet-121 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 2.054 ms | 16 - 16 MB | FP16 | NPU | [DenseNet-121.onnx](https://huggingface.co/qualcomm/DenseNet-121/blob/main/DenseNet-121.onnx) |
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  ## Installation
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  ```bash
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  python -m qai_hub_models.models.densenet121.export
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  ```
 
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  ```
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+ Profiling Results
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+ ------------------------------------------------------------
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+ DenseNet-121
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+ Device : Samsung Galaxy S23 (13)
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+ Runtime : TFLITE
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+ Estimated inference time (ms) : 1.9
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+ Estimated peak memory usage (MB): [0, 6]
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+ Total # Ops : 312
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+ Compute Unit(s) : NPU (312 ops)
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  ```
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  Get more details on DenseNet-121's performance across various devices [here](https://aihub.qualcomm.com/models/densenet121).
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  Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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+
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  ## License
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+ * The license for the original implementation of DenseNet-121 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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+
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+
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  ## References
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  * [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
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  * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/densenet.py)
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+
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  ## Community
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  * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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  * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).