Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+license_link: >-
+  https://github.st.com/AIS/stm32ai-modelzoo/raw/master/neural-style-transfer/LICENSE.md
+---
+# Xinet_picasso_muse
+
+## **Use case** : `Neural style transfer`
+
+# Model description
+
+Xinet_picasso_muse is a lightweight Neural Style Transfer approach based on [XiNets](https://openaccess.thecvf.com/content/ICCV2023/papers/Ancilotto_XiNet_Efficient_Neural_Networks_for_tinyML_ICCV_2023_paper.pdf), 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](https://www.computer.org/csdl/proceedings-article/percom-workshops/2024/10502435/1Wnrsw29p5e) |
+
+
+
+## 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/neural_style_transfer/Public_pretrainedmodel_public_dataset/coco_2017_80_classes_picasso/xinet_a75_picasso_muse_160/xinet_a75_picasso_muse_160_nomp.tflite)  | 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](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/neural_style_transfer/Public_pretrainedmodel_public_dataset/coco_2017_80_classes_picasso/xinet_a75_picasso_muse_160/xinet_a75_picasso_muse_160_nomp.tflite)  | 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
+
+<a id="1">[1]</a> "Painting the Starry Night using XiNets" Alberto Ancilotto, Elisabetta Farella - 2024 IEEE International Conference on Pervasive Computing [Link](https://www.computer.org/csdl/proceedings-article/percom-workshops/2024/10502435/1Wnrsw29p5e)