Release AI-ModelZoo-4.0.0:

README.md

@@ -1,3 +1,111 @@
----
-license: apache-2.0
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+---
+license: apache-2.0
+pipeline_tag: image-classification
+---
+# HardNet
+
+## **Use case** : `Image classification`
+
+# Model description
+
+
+Harmonic DenseNet (HardNet) is a memory-efficient variant of DenseNet that optimizes for both **computational efficiency and memory access cost**. It introduces a harmonic pattern in the dense connections to reduce redundant feature computations.
+
+HardNet features **harmonic dense connections** that reduce connection patterns to minimize memory bandwidth, while maintaining the benefits of DenseNet's feature reuse. The architecture combines **depthwise separable convolutions** with dense blocks for enhanced efficiency.
+
+Designed for practical hardware deployment, HardNet provides DenseNet-like feature richness with lower memory cost on edge devices.
+
+(source: https://arxiv.org/abs/1909.00948)
+
+The model is quantized to **int8** using **ONNX Runtime** and exported for efficient deployment.
+
+## Network information
+
+
+| Network Information | Value |
+|--------------------|-------|
+| Framework          | Torch |
+| MParams            | ~3.43 M |
+| Quantization       | Int8 |
+| Provenance         | https://github.com/PingoLH/Pytorch-HarDNet |
+| Paper              | https://arxiv.org/abs/1909.00948 |
+
+## 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 |
+|-------|---------|--------|------------|--------|--------------|--------------|---------------|----------------------|
+| [hardnet39ds_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/hardnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/hardnet39ds_pt_224/hardnet39ds_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6 | 1476.12 | 0 | 3516.67 | 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 |
+|-------|---------|--------|--------|------------|-------|-----------------|-------------------|---------------------|
+| [hardnet39ds_pt_224](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/hardnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/hardnet39ds_pt_224/hardnet39ds_pt_224_qdq_int8.onnx) | Imagenet | Int8 | 224×224×3 | STM32N6570-DK | NPU/MCU | 65.81 | 15.19 | 3.0.0  |
+
+
+
+### Accuracy with Imagenet dataset
+
+| Model | Format | Resolution | Top 1 Accuracy |
+| --- | --- | --- | --- |
+| [hardnet39ds_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/hardnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/hardnet39ds_pt_224/hardnet39ds_pt_224.onnx) | Float | 224x224x3 | 74.38 % |
+| [hardnet39ds_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/hardnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/hardnet39ds_pt_224/hardnet39ds_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 73.61 % |
+
+
+Dataset details: [link](https://www.image-net.org)
+Number of classes: 1000.
+To perform the quantization, we calibrated the activations with a random subset of the training set.
+For the sake of simplicity, the accuracy reported here was estimated on the 50000 labelled images of the validation set.
+
+| Model | Format | Resolution | Top 1 Accuracy |
+| --- | --- | --- | --- |
+| [hardnet39ds_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/hardnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/hardnet39ds_pt_224/hardnet39ds_pt_224.onnx) | Float | 224x224x3 | 74.38 % |
+| [hardnet39ds_pt](https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification/hardnet_pt/Public_pretrainedmodel_public_dataset/Imagenet/hardnet39ds_pt_224/hardnet39ds_pt_224_qdq_int8.onnx) | Int8 | 224x224x3 | 73.61 % |
+
+
+
+## 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**: HarDNet — https://github.com/PingoLH/Pytorch-HarDNet