Manufacturing and Metro Vision apps: Sync main branch with latest changes from release-2026.0.0 (open-edge-platform#2154)

Co-authored-by: Athina Saha <athina.saha@intel.com>
Co-authored-by: svamsik <vamsi.krishna.sammeta@intel.com>

Author: 3 people

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/_assets/download_coco_datasets1.png

@@ -0,0 +1,202 @@
+# Export and Optimize Geti Model
+
+## Overview
+
+This guide starts by downloading the trained YOLOX PyTorch weights from Intel Geti and the COCO dataset used during training. A workspace is then set up and the [Training Extensions](https://github.com/open-edge-platform/training_extensions) repository is cloned, which provides the conversion script. After installing the required Python and Rust dependencies, the `export_and_optimize.py` script is run to convert the model to OpenVINO IR format — producing a full-precision FP32 model and an INT8 post-training quantized model optimized for Intel hardware.
+
+---
+
+## Prerequisites
+
+Before you begin, ensure you have the following:
+
+- A trained model exported from Intel Geti as a **PyTorch weights file** (`.pth`)
+
+  ![Download PyTorch Weights from Intel Geti](../_assets/download_model_pytorch_weights.png)
+
+  *Note: Image is for illustration purposes only.*
+
+- A **COCO-format dataset** (`.zip`) used during training (required for post-training optimization)
+
+  ![Download COCO Dataset - Step 1](../_assets/download_coco_datasets1.png)
+
+  ![Download COCO Dataset - Step 2](../_assets/download_coco_datasets2.png)
+
+  *Note: Images are for illustration purposes only.*
+
+- [Git](https://git-scm.com/) installed
+- Internet access to download dependencies
+
+---
+
+## Step 1: Set Up the Workspace
+
+Create the working directory structure:
+
+```bash
+mkdir generate_model
+cd generate_model
+
+mkdir model
+mkdir coco_dataset
+mkdir output
+```
+
+| Directory      | Purpose                                      |
+|----------------|----------------------------------------------|
+| `model/`       | Stores the downloaded PyTorch weights file   |
+| `coco_dataset/`| Stores the COCO dataset used for optimization|
+| `output/`      | Stores the exported and optimized model files|
+
+---
+
+## Step 2: Add Model Weights and Dataset
+
+### Copy and Extract the PyTorch Model
+
+Place the downloaded `Pytorch_model.zip` file into the `model/` directory and extract it:
+
+```bash
+# Copy Pytorch_model.zip into the model directory, then unzip
+cp /path/to/Pytorch_model.zip model/
+cd model
+unzip Pytorch_model.zip
+cd ..
+```
+
+After extraction, the `model/` directory should contain a `weights.pth` file.
+
+### Copy and Extract the COCO Dataset
+
+Place the downloaded COCO dataset archive into the `coco_dataset/` directory and extract it:
+
+```bash
+# Copy the COCO dataset zip into the coco_dataset directory, then unzip
+cp /path/to/<coco_dataset>.zip coco_dataset/
+cd coco_dataset
+unzip <coco_dataset>.zip
+cd ..
+```
+
+After extraction, the `coco_dataset/` directory should follow the standard COCO layout:
+
+```
+coco_dataset/
+├── annotations/
+└── images/
+```
+
+---
+
+## Step 3: Clone the Training Extensions Repository
+
+```bash
+git clone https://github.com/open-edge-platform/training_extensions.git
+```
+
+---
+
+## Step 4: Install Dependencies
+
+### Install `uv` (Python Package Manager)
+
+```bash
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source $HOME/.local/bin/env
+```
+
+### Install Rust Toolchain (required by some dependencies)
+
+```bash
+curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
+```
+
+---
+
+## Step 5: Set Up the Python Environment
+
+Navigate to the `library` directory within the cloned repository and check out the required branch:
+
+```bash
+cd training_extensions/library
+git checkout kp/test_yolox
+```
+
+Create and activate a virtual environment, then sync all dependencies:
+
+```bash
+uv venv
+source .venv/bin/activate
+source "$HOME/.cargo/env"
+uv sync
+```
+
+---
+
+## Step 6: Export and Optimize the Model
+
+Run the `export_and_optimize.py` script with the appropriate paths and model configuration:
+
+```bash
+python export_and_optimize.py \
+    --weights        /path/to/model/weights.pth \
+    --source_dataset /path/to/coco_dataset \
+    --output_dir     /path/to/output \
+    --model_name     yolox_tiny
+```
+
+### Arguments
+
+| Argument           | Required | Description                                                    |
+|--------------------|----------|----------------------------------------------------------------|
+| `--weights`        | Yes      | Path to the PyTorch weights file (`.pth`)                      |
+| `--source_dataset` | Yes      | Path to the COCO dataset directory                             |
+| `--output_dir`     | Yes      | Directory where exported and optimized model files are saved   |
+| `--model_name`     | Yes      | Model variant to use. Supported values: `yolox_tiny`, `yolox_s`, `yolox_l`, `yolox_x` (default: `yolox_tiny`) |
+
+### Example with Absolute Paths
+
+Assuming the workspace is located at `~/generate_model`:
+
+```bash
+python export_and_optimize.py \
+    --weights        ~/generate_model/model/weights.pth \
+    --source_dataset ~/generate_model/coco_dataset \
+    --output_dir     ~/generate_model/output \
+    --model_name     yolox_tiny
+```
+
+---
+
+## Output
+
+After the script completes, the `output/` directory will contain the exported and optimized model files ready for deployment in the Pallet Defect Detection pipeline:
+
+```
+output/
+├── otx-workspace/
+│   ├── exported_model.xml    # FP32 – full-precision exported model
+│   └── optimized_model.xml   # INT8 – post-training quantized model
+```
+
+| File                  | Precision | Description                                                              |
+|-----------------------|-----------|--------------------------------------------------------------------------|
+| `exported_model.xml`  | FP32      | Full-precision model exported directly from the PyTorch weights          |
+| `optimized_model.xml` | INT8      | Post-training quantized model optimized using the COCO dataset           |
+
+Both files can be used directly with the OpenVINO inference engine. The INT8 model (`optimized_model.xml`) offers faster inference with reduced memory footprint, while the FP32 model (`exported_model.xml`) retains full numerical precision.
+
+![Generated Model Output](../_assets/generated_model.png)
+
+*Note: Image is for illustration purposes only.*
+
+---
+
+## Troubleshooting
+
+| Issue                              | Resolution                                                                 |
+|------------------------------------|----------------------------------------------------------------------------|
+| `uv: command not found`            | Re-run `source $HOME/.local/bin/env` or open a new terminal session        |
+| Rust compilation errors            | Ensure `source "$HOME/.cargo/env"` was run after the Rust installation     |
+| Dataset not found                  | Verify the COCO dataset was extracted and the `annotations/` folder exists |
+| Incorrect model output             | Confirm `--model_name` matches the architecture used during Geti training  |

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/_assets/download_coco_datasets2.png

@@ -84,6 +84,7 @@ get-started
 how-to-guides
 api-reference
 troubleshooting
+export-and-optimize-geti-model
 release-notes
 
 :::

manufacturing-ai-suite/industrial-edge-insights-vision/docs/user-guide/pallet-defect-detection/_assets/download_model_pytorch_weights.png

@@ -97,6 +97,12 @@ privileged_access_required: true
 
 To perform inferencing on an NPU device (for platforms with NPU accelerators such as Ultra Core processors), ensure you have completed the required pre-requisites. Refer to the relevant [DL Streamer instructions](https://docs.openedgeplatform.intel.com/dev/edge-ai-libraries/dlstreamer/dev_guide/advanced_install/advanced_install_guide_prerequisites.html#optional-prerequisite-2-install-intel-npu-drivers) to install Intel NPU drivers.
 
+## NPU Inference Failures with Geti-Trained Models
+
+If you experience errors or failures when running an NPU workload with a model trained in Intel Geti, this may be caused by **Non-Maximum Suppression (NMS)** being embedded within the model graph. The NPU does not support dynamic shapes, and NMS operations with dynamic output shapes are incompatible with NPU execution.
+
+**Resolution**: Follow the [Export and Optimize Geti Model](./how-to-guides/export-and-optimize-geti-model.md) guide to generate a model with NMS removed from the model graph. NMS will then be handled by DL Streamer.
+
 ## Unable to parse JSON payload due to missing `jq` package
 
 While running the `sample_start.sh` script, you may encounter