Xinet_picasso_muse
Use case : Neural style transfer
Model description
Xinet_picasso_muse is a lightweight Neural Style Transfer approach based on XiNets, neural networks especially developed for microcontrollers and embedded applications. It has been trained using the COCO dataset for content images and the painting La Muse of Pablo Picasso for style image. This model achieves an extremely lightweight transfer style mechanism and high-quality stylized outputs, significantly reducing computational complexity.
Xinet_picasso_muse is implemented initially in Pytorch and is quantized in int8 format using tensorflow lite converter. To reach a better performances, the mirror padding ops have been replaced with zero padding ops.
Network information
| Network Information | Value |
|---|---|
| Framework | Tensorflow |
| Quantization | int8 |
| Paper | Link to Paper |
Recommended platform
| Platform | Supported | Recommended |
|---|---|---|
| STM32L0 | [] | [] |
| STM32L4 | [] | [] |
| STM32U5 | [] | [] |
| STM32MP1 | [] | [] |
| STM32MP2 | [] | [] |
| STM32N6 | [x] | [x] |
Performances
Metrics
Measures are done with default STM32Cube.AI configuration with enabled input / output allocated option.
Reference NPU memory footprint based on COCO dataset
| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STM32Cube.AI version | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|---|
| Xinet picasso muse | COCO/Picasso | Int8 | 160x160x3 | STM32N6 | 2685.38 | 600.0 | 851.86 | 10.2.0 | 2.2.0 |
Reference NPU inference time based on COCO Person dataset
| Model | Dataset | Format | Resolution | Board | Execution Engine | Inference time (ms) | Inf / sec | STM32Cube.AI version | STEdgeAI Core version |
|---|---|---|---|---|---|---|---|---|---|
| Xinet picasso muse | COCO/Picasso | Int8 | 160x160x3 | STM32N6570-DK | NPU/MCU | 61.96 | 16.13 | 10.2.0 | 2.2.0 |
Retraining and Integration in a Simple Example
Retraining and deployment services are currently not provided for this model. They should be supported in the future releases.
References
[1] "Painting the Starry Night using XiNets" Alberto Ancilotto, Elisabetta Farella - 2024 IEEE International Conference on Pervasive Computing Link