Tags: STMicroelectronics/stm32ai-modelzoo

Release AI-ModelZoo-4.1.0:

- Support of STEdgeAI Core v4.0.0.
- Updated Audio Event Detection (AED) to support deployment on STM32U3.
- Added support for the YOLO26 model.
- Multiple bug fixes and overall quality improvements.


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Updates :

- Add new arc fault detection models.
- Update image classification Pytorch YAML and README files.


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-4.0.0:

- Major PyTorch support for Image Classification (IC) and Object Detection (OD)
- Support of STEdgeAI Core v3.0.0
- New training and evaluation scripts for PyTorch models
- Expanded model selection and improved documentation
- Unified workflow for TensorFlow and PyTorch
- Performance and usability improvements
- New use cases: Face Detection (FD), Arc Fault Detection (AFD), Re-Identification (ReID)
- New mixed precision models (Weights 4-bits, Activations 8-bits) for IC and OD use cases
- Support for Keras 3.8.0, TensorFlow 2.18.0, PyTorch 2.7.1, and ONNX 1.16.1
- Python software architecture rework
- Docker-based setup available, with a ready-to-use image including the full software stack


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-3.2.0:

- Support of STEdgeAI Core v2.2.0 (STM32Cube.AI v10.2.0).
- Support of X-Linux-AI v6.1.0 support for MPU.
- New use cases added: StyleTransfer and FastDepth.
- New models added: Face Detection, available in the Object Detection use case, and Face Landmarks, available in the Pose Estimation use case.
- Architecture and codebase clean-up.


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-3.1.0:

- Support for STEdgeAI Core v2.1.0 (STM32Cube.AI v10.1.0).
- Application code for STM32N6 board is now directly available in the STM32 model zoo repository; eliminating the need for separate downloads.
- Support of On device evaluation and On device prediction on the STM32N6570-DK boards integrated in evaluation and prediction services.
- More models supported : Yolov11, LSTM model added in the Speech Enhancement, ST Yolo X variants.
- ClearML support.
- A few bug fixes and improvements like proper imports, OD metrics alignments.

Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-3.0.0:

- Included additional models compatible with the STM32N6570-DK board.
- Expanded models in all use cases.
- Expanded use case support to include Instance Segmentation and Speech Enhancement.
- Added Pytorch support through the speech enhancement Use Case.
- Model Zoo hosted on Hugging Face

Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-2.1.1:

- Fixed some bugs.
- Added more detailed information in README files.
- Updated development cloud benchmark notebook to include MPU boards.


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-2.1.0:

- Included additional models compatible with the STM32MP257F-DK2 board.
- Added support for per-tensor quantization.
- Integrated support for ONNX model quantization and evaluation.
- Included support for STEdgeAI (STM32Cube.AI v9.1.0 and subsequent versions).
- Expanded use case support to include Pose Estimation and Semantic Segmentation.
- Standardized logging information for a unified experience.


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-2.0.1:

   - Disclosed some ST object detection models: `st_yolo_lc_v1` and `ssd_mobilenet_v2_fpnlite` with various resolutions.
   - Disclosed some ST image classification models: `st_efficientnet_lc_v1`, `st_fdmobilenet_v1`, `st_resnet_8_hybrid_v1`, and `st_resnet_8_hybrid_v2` in different resolutions.
   - Fixed minor bugs and updated README documentation.


Signed-off-by: khaoula boutiche <khaoula.boutiche@st.com>

Release AI-ModelZoo-2.0.0

- Aligned and uniformed architecture for all the use case folders.
- Modular design to run different operation modes (training, benchmarking, evaluation, deployment, quantization) independently or with an option of chaining multiple modes in a single launch.
- A single entry point to the code, with a single configuration file to configure all the modes.
- Added Bring Your Own Model (BYOM) feature.
- More training capabilities proposed (transfer learning, fine tuning,
training from scratch).
- Addition of Object Detection models.