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.
More details on model performance across various devices, can be found here.
Model Details
- Model Type: Model_use_case.semantic_segmentation
- Model Stats:
- Model checkpoint: BGNet
- Input resolution: 416x416
- Number of parameters: 77.8M
- Model size: 297 MB
| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |
|---|---|---|---|---|---|---|---|---|
| BGNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 118.319 ms | 1 - 126 MB | NPU | -- |
| BGNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN | 116.819 ms | 2 - 11 MB | NPU | -- |
| BGNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 34.374 ms | 1 - 209 MB | NPU | -- |
| BGNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN | 41.165 ms | 2 - 50 MB | NPU | -- |
| BGNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 22.942 ms | 1 - 19 MB | NPU | -- |
| BGNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN | 19.894 ms | 2 - 4 MB | NPU | -- |
| BGNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 118.319 ms | 1 - 126 MB | NPU | -- |
| BGNet | float | SA7255P ADP | Qualcomm® SA7255P | QNN | 116.819 ms | 2 - 11 MB | NPU | -- |
| BGNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 23.032 ms | 1 - 19 MB | NPU | -- |
| BGNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN | 19.82 ms | 2 - 4 MB | NPU | -- |
| BGNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 37.993 ms | 1 - 99 MB | NPU | -- |
| BGNet | float | SA8295P ADP | Qualcomm® SA8295P | QNN | 34.723 ms | 2 - 19 MB | NPU | -- |
| BGNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 23.229 ms | 1 - 20 MB | NPU | -- |
| BGNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN | 19.833 ms | 2 - 4 MB | NPU | -- |
| BGNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | TFLITE | 22.84 ms | 1 - 19 MB | NPU | -- |
| BGNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | QNN | 19.968 ms | 2 - 29 MB | NPU | -- |
| BGNet | float | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 Mobile | ONNX | 20.487 ms | 0 - 173 MB | NPU | -- |
| BGNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 16.768 ms | 0 - 234 MB | NPU | -- |
| BGNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN | 14.738 ms | 2 - 76 MB | NPU | -- |
| BGNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 14.886 ms | 4 - 83 MB | NPU | -- |
| BGNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | TFLITE | 15.45 ms | 1 - 126 MB | NPU | -- |
| BGNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | QNN | 12.583 ms | 2 - 63 MB | NPU | -- |
| BGNet | float | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite Mobile | ONNX | 15.563 ms | 2 - 65 MB | NPU | -- |
| BGNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 20.261 ms | 2 - 2 MB | NPU | -- |
| BGNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 22.284 ms | 154 - 154 MB | 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
References
Community
- Join our AI Hub Slack community to collaborate, post questions and learn more about on-device AI.
- For questions or feedback please reach out to us.
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
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