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README.md

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+---
+library_name: pytorch
+license: other
+tags:
+- real_time
+- android
+pipeline_tag: image-segmentation
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/bgnet/web-assets/model_demo.png)
+
+# BGNet: Optimized for Mobile Deployment
+## Segment images in real-time on device
+
+
+BGNet or Boundary-Guided Network, is a model designed for camouflaged object detection. It leverages edge semantics to enhance the representation learning process, making it more effective at identifying objects that blend into their surroundings
+
+This model is an implementation of BGNet found [here](https://github.com/thograce/bgnet).
+
+
+ More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/bgnet).
+
+### Model Details
+
+- **Model Type:** Semantic segmentation
+- **Model Stats:**
+  - Model checkpoint: BGNet
+  - Input resolution: 416x416
+  - Number of parameters: 77.8M
+  - Model size: 297 MB
+
+| Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| BGNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 22.787 ms | 0 - 18 MB | FP16 | NPU | -- |
+| BGNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 19.881 ms | 2 - 5 MB | FP16 | NPU | -- |
+| BGNet | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 24.125 ms | 1 - 159 MB | FP16 | NPU | -- |
+| BGNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 16.802 ms | 7 - 243 MB | FP16 | NPU | -- |
+| BGNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 14.693 ms | 2 - 23 MB | FP16 | NPU | -- |
+| BGNet | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 17.726 ms | 4 - 85 MB | FP16 | NPU | -- |
+| BGNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 15.382 ms | 1 - 126 MB | FP16 | NPU | -- |
+| BGNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 16.747 ms | 2 - 69 MB | FP16 | NPU | -- |
+| BGNet | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 17.675 ms | 2 - 74 MB | FP16 | NPU | -- |
+| BGNet | SA7255P ADP | SA7255P | TFLITE | 855.393 ms | 0 - 126 MB | FP16 | NPU | -- |
+| BGNet | SA7255P ADP | SA7255P | QNN | 871.849 ms | 2 - 12 MB | FP16 | NPU | -- |
+| BGNet | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 22.87 ms | 1 - 19 MB | FP16 | NPU | -- |
+| BGNet | SA8255 (Proxy) | SA8255P Proxy | QNN | 19.895 ms | 2 - 4 MB | FP16 | NPU | -- |
+| BGNet | SA8295P ADP | SA8295P | TFLITE | 37.995 ms | 1 - 99 MB | FP16 | NPU | -- |
+| BGNet | SA8295P ADP | SA8295P | QNN | 34.745 ms | 2 - 19 MB | FP16 | NPU | -- |
+| BGNet | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 23.03 ms | 0 - 17 MB | FP16 | NPU | -- |
+| BGNet | SA8650 (Proxy) | SA8650P Proxy | QNN | 19.947 ms | 2 - 4 MB | FP16 | NPU | -- |
+| BGNet | SA8775P ADP | SA8775P | TFLITE | 42.229 ms | 1 - 126 MB | FP16 | NPU | -- |
+| BGNet | SA8775P ADP | SA8775P | QNN | 39.85 ms | 2 - 12 MB | FP16 | NPU | -- |
+| BGNet | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 855.393 ms | 0 - 126 MB | FP16 | NPU | -- |
+| BGNet | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 871.849 ms | 2 - 12 MB | FP16 | NPU | -- |
+| BGNet | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 22.931 ms | 1 - 20 MB | FP16 | NPU | -- |
+| BGNet | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 20.001 ms | 2 - 4 MB | FP16 | NPU | -- |
+| BGNet | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 42.229 ms | 1 - 126 MB | FP16 | NPU | -- |
+| BGNet | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 39.85 ms | 2 - 12 MB | FP16 | NPU | -- |
+| BGNet | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 33.915 ms | 1 - 215 MB | FP16 | NPU | -- |
+| BGNet | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 35.538 ms | 2 - 51 MB | FP16 | NPU | -- |
+| BGNet | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 20.347 ms | 2 - 2 MB | FP16 | NPU | -- |
+| BGNet | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 24.069 ms | 156 - 156 MB | FP16 | NPU | -- |
+
+
+
+
+## License
+* The license for the original implementation of BGNet can be found
+  [here](This model's original implementation does not provide a LICENSE.).
+* 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)
+
+
+
+## References
+* [BGNet: Boundary-Guided Camouflaged Object Detection (IJCAI 2022)](https://arxiv.org/abs/2207.00794)
+* [Source Model Implementation](https://github.com/thograce/bgnet)
+
+
+
+## Community
+* Join [our AI Hub Slack community](https://qualcomm-ai-hub.slack.com/join/shared_invite/zt-2d5zsmas3-Sj0Q9TzslueCjS31eXG2UA#/shared-invite/email) to collaborate, post questions and learn more about on-device AI.
+* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
+
+## Usage and Limitations
+
+Model may not be used for or in connection with any of the following applications:
+
+- Accessing essential private and public services and benefits;
+- Administration of justice and democratic processes;
+- Assessing or recognizing the emotional state of a person;
+- Biometric and biometrics-based systems, including categorization of persons based on sensitive characteristics;
+- Education and vocational training;
+- Employment and workers management;
+- Exploitation of the vulnerabilities of persons resulting in harmful behavior;
+- General purpose social scoring;
+- Law enforcement;
+- Management and operation of critical infrastructure;
+- Migration, asylum and border control management;
+- Predictive policing;
+- Real-time remote biometric identification in public spaces;
+- Recommender systems of social media platforms;
+- Scraping of facial images (from the internet or otherwise); and/or
+- Subliminal manipulation