Release AI-ModelZoo-4.0.0

README.md

@@ -1,3 +1,110 @@
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-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: image-classification
+---
+# SEMnasNet
+
+## **Use case** : `Image classification`
+
+# Model description
+
+
+SEMnasNet combines the MnasNet architecture with **Squeeze-and-Excitation (SE) blocks**, adding channel attention mechanisms to the NAS-derived architecture for improved accuracy.
+
+The architecture builds on MnasNet's **NAS-derived efficient design** and adds **Squeeze-and-Excitation blocks** for channel attention and feature recalibration. **Adaptive feature weighting** emphasizes informative channels, with SE blocks boosting accuracy with minimal overhead.
+
+SEMnasNet achieves the **highest accuracy** in the model zoo (75.38% Top-1) with excellent quantization stability (0.37% drop), making it the best choice for accuracy-critical applications.
+
+(source: https://arxiv.org/abs/1807.11626, https://arxiv.org/abs/1709.01507)
+
+The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment.
+
+## Network information
+
+
+| Network Information | Value |
+|--------------------|-------|
+| Framework          | Torch |
+| MParams            | ~4.04 M |
+| Quantization       | Int8 |
+| Provenance         | https://github.com/huggingface/pytorch-image-models |
+| Paper              | https://arxiv.org/abs/1807.11626 |
+
+## Network inputs / outputs
+
+
+For an image resolution of NxM and P classes
+
+| Input Shape | Description |
+| ----- | ----------- |
+| (1, N, M, 3) | Single NxM RGB image with UINT8 values between 0 and 255 |
+
+| Output Shape | Description |
+| ----- | ----------- |
+| (1, P) | Per-class confidence for P classes in FLOAT32|
+
+
+## Recommended platforms
+
+
+| Platform | Supported | Recommended |
+|----------|-----------|-----------|
+| STM32L0  |[]|[]|
+| STM32L4  |[]|[]|
+| STM32U5  |[]|[]|
+| STM32H7  |[]|[]|
+| STM32MP1 |[]|[]|
+| STM32MP2 |[]|[]|
+| STM32N6  |[x]|[x]|
+
+# Performances
+
+## Metrics
+
+- Measures are done with default STEdgeAI Core configuration with enabled input / output allocated option.
+- All the models are trained from scratch on Imagenet dataset 
+
+### Reference **NPU** memory footprint on Imagenet dataset (see Accuracy for details on dataset)
+| Model | Dataset | Format | Resolution | Series | Internal RAM (KiB) | External RAM (KiB) | Weights Flash (KiB) | STEdgeAI Core version |
+|-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------|
+| [semnasnet100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 2058 | 0 | 4133.38 | 3.0.0 |
+
+### Reference **NPU**  inference time on Imagenet dataset (see Accuracy for details on dataset)
+
+| Model  |  Dataset  | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec |  STEdgeAI Core version |
+|--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------|
+| [semnasnet100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 37.63 | 26.57 | 3.0.0  |
+
+| Model  |  Dataset  | Format | Resolution  | Board            | Execution Engine | Inference time (ms) | Inf / sec |  STEdgeAI Core version |
+|--------|---------|--------|--------|-------------|------------------|------------------|---------------------|-------------------------|
+| [semnasnet100_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 37.63 | 26.57 | 3.0.0  |
+
+
+### Accuracy with Imagenet dataset
+
+| Model | Format | Resolution | Top 1 Accuracy |
+| --- | --- | --- | --- |
+| [semnasnet100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224.onnx) | Float | 224x224x3 | 75.75 % |
+| [semnasnet100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 75.38 % |
+
+
+| Model | Format | Resolution | Top 1 Accuracy |
+| --- | --- | --- | --- |
+| [semnasnet100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224.onnx) | Float | 224x224x3 | 75.75 % |
+| [semnasnet100_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/semnasnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/semnasnet100_pt_224/semnasnet100_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 75.38 % |
+
+
+
+## Retraining and Integration in a simple example:
+
+Please refer to the stm32ai-modelzoo-services GitHub [here](https://github.com/STMicroelectronics/stm32ai-modelzoo-services)
+
+
+
+# References
+
+<a id="1">[1]</a> - **Dataset**: Imagenet (ILSVRC 2012) — https://www.image-net.org/
+
+<a id="2">[2]</a> - **Model (MnasNet)**: MnasNet — https://arxiv.org/abs/1807.11626
+
+<a id="3">[3]</a> - **Model (SE-Net)**: Squeeze-and-Excitation Networks — https://arxiv.org/abs/1709.01507