description: Learn to export Ultralytics YOLOv8 models to Sony's IMX500 format to optimize your models for efficient deployment.
keywords: YOLOv8, IMX500, MCT, model export, quantization, pruning, deep learning optimization, Raspberry Pi AI Camera, edge AI, PyTorch
---
comments: true
description: Learn to export Ultralytics YOLOv8 models to Sony's IMX500 format to optimize your models for efficient deployment.
keywords: YOLOv8, IMX500, MCT, model export, quantization, pruning, deep learning optimization, Raspberry Pi AI Camera, edge AI, PyTorch
---
# Learn to Export to IMX500 Format From YOLOv8 Model
@ -26,13 +26,13 @@ The IMX500 works with quantized models. Quantization makes models smaller and fa
**IMX500 Key Features:**
***Metadata Output:** Instead of transmitting full images, the IMX500 outputs only metadata, minimizing data size, reducing bandwidth, and lowering costs.
***Addresses Privacy Concerns:** By processing data on the device, the IMX500 addresses privacy concerns, ideal for human-centric applications like person counting and occupancy tracking.
***Real-time Processing:** Fast, on-sensor processing supports real-time decisions, perfect for edge AI applications such as autonomous systems.
-**Metadata Output:** Instead of transmitting full images, the IMX500 outputs only metadata, minimizing data size, reducing bandwidth, and lowering costs.
-**Addresses Privacy Concerns:** By processing data on the device, the IMX500 addresses privacy concerns, ideal for human-centric applications like person counting and occupancy tracking.
-**Real-time Processing:** Fast, on-sensor processing supports real-time decisions, perfect for edge AI applications such as autonomous systems.
**Before You Begin:** For best results, ensure your YOLOv8 model is well-prepared for export by following our [Model Training Guide](https://docs.ultralytics.com/modes/train/), [Data Preparation Guide](https://docs.ultralytics.com/datasets/), and [Hyperparameter Tuning Guide](https://docs.ultralytics.com/guides/hyperparameter-tuning/).
yolo val model=yolov8n_imx500_model.onnx data=coco128.yaml
```
!!! note
It might take longer to export with GPTQ quantization based on the hardware specifications.
The export process will create an ONNX model for quantization validation, along with a directory named imx500\_model. This directory will include the packerOut.zip file, which is essential for packaging the model for deployment on the IMX500 hardware. Additionally, the imx500\_model folder will contain a text file listing all the labels associated with the model and other relevant metadata.
The export process will create an ONNX model for quantization validation, along with a directory named imx500_model. This directory will include the packerOut.zip file, which is essential for packaging the model for deployment on the IMX500 hardware. Additionally, the imx500_model folder will contain a text file listing all the labels associated with the model and other relevant metadata.
```bash
├── yolov8n_imx500_model
│ ├── dnnParams.xml
│ ├── labels.txt
│ ├── packerOut.zip
│ ├── yolov8n_imx500_model_MemoryReport.json
│ └── yolov8n_imx500_model.pbtxt
└── yolov8n_imx500_model.onnx
├── yolov8n_imx500_model
│ ├── dnnParams.xml
│ ├── labels.txt
│ ├── packerOut.zip
│ ├── yolov8n_imx500_model_MemoryReport.json
│ └── yolov8n_imx500_model.pbtxt
└── yolov8n_imx500_model.onnx
```
## Arguments
When exporting a model to IMX500 format , you can specify various arguments:
| `format` | `'imx500'` | Format to export to (imx500) |
| `gptq` | `False` | Use Gradient-based Post-Training Quantization (GPTQ) instead of PTQ |
## Using IMX500 Export in Deployment
@ -110,7 +110,7 @@ After exporting YOLOv8 models to Sony IMX500 format, they can be deployed to Ras
Make sure you have the below hardware:
1. Raspberry Pi 5 or Raspberry Pi 4 Model B
1. Raspberry Pi 5 or Raspberry Pi 4 Model B
2. Raspberry Pi AI Camera
Connect the Raspberry Pi AI camera to the 15-pin MIPI CSI connector on the Raspberry Pi and power on the Raspberry Pi
@ -123,56 +123,56 @@ Connect the Raspberry Pi AI camera to the 15-pin MIPI CSI connector on the Raspb
Step 1: Open a terminal window and execute the following commands to update the Raspberry Pi software to the latest version.
```bash
sudo apt update && sudo apt full-upgrade
```bash
sudo apt update && sudo apt full-upgrade
```
Step 2: Install IMX500 firmware which is required to operate the IMX500 sensor along with a packager tool.
```bash
sudo apt install imx500-all imx500-tools
```bash
sudo apt install imx500-all imx500-tools
```
Step 3: Install prerequisites to run \`picamera2\` application. We will use this applicaiton later for the deployment process.
```bash
sudo apt install python3-opencv python3-munkres
```bash
sudo apt install python3-opencv python3-munkres
```
Step 4: Reboot Raspberry Pi for the changes to take into effect
```bash
sudo reboot
```bash
sudo reboot
```
### Package Model and Deploy to AI Camera
### Package Model and Deploy to AI Camera
After obtaining `packerOut.zip` from the IMX500 conversion process, you can pass this file into the packager tool to obtain an RPK file. This file can then be deployed directly to the AI Camera using `picamera2`.
MCT also supports various quantization schemes for weights and activations:
1. Power-of-Two (hardware-friendly)
1. Power-of-Two (hardware-friendly)
2. Symmetric
3. Uniform
### Main Features
1. **Graph Optimizations**: Transforms models into more efficient versions by folding layers like batch normalization into preceding layers.
2. **Quantization Parameter Search**: Minimizes quantization noise using metrics like Mean-Square-Error, No-Clipping, and Mean-Average-Error.
1. **Graph Optimizations**: Transforms models into more efficient versions by folding layers like batch normalization into preceding layers.
2. **Quantization Parameter Search**: Minimizes quantization noise using metrics like Mean-Square-Error, No-Clipping, and Mean-Average-Error.
3. **Advanced Quantization Algorithms**:
**Shift Negative Correction**: Addresses performance issues from symmetric activation quantization.
**Outliers Filtering**: Uses z-score to detect and remove outliers.
**Clustering**: Utilizes non-uniform quantization grids for better distribution matching.
**Mixed-Precision Search**: Assigns different quantization bit-widths per layer based on sensitivity.
4. Visualization**: Use TensorBoard to observe model performance insights, quantization phases, and bit-width configurations.
**Mixed-Precision Search**: Assigns different quantization bit-widths per layer based on sensitivity.
4. Visualization\*\*: Use TensorBoard to observe model performance insights, quantization phases, and bit-width configurations.
#### Enhanced Post-Training Quantization (EPTQ)
@ -225,22 +225,22 @@ MCT introduces structured, hardware-aware model pruning designed for specific ha
## Benefits of Using MCT
1. **Model Size Reduction**: Significantly reduces the model size through quantization and pruning.
2. **Inference Speedup**: Improves inference speed by optimizing computations.
3. **Hardware Compatibility**: Generates models optimized for specific hardware architectures.
4. **Advanced Algorithms**: Utilizes state-of-the-art quantization and pruning algorithms.
1. **Model Size Reduction**: Significantly reduces the model size through quantization and pruning.
2. **Inference Speedup**: Improves inference speed by optimizing computations.
3. **Hardware Compatibility**: Generates models optimized for specific hardware architectures.
4. **Advanced Algorithms**: Utilizes state-of-the-art quantization and pruning algorithms.
5. **Ease of Integration**: Seamlessly integrates with PyTorch models.
### **Real-World Use Cases**
**Export to** IMX500 format has wide applicability across industries. Here are some examples:
***Edge AI and IoT**: Enable object detection on drones or security cameras, where real-time processing on low-power devices is essential.
***Wearable Devices**: Deploy models optimized for small-scale AI processing on health-monitoring wearables.
***Smart Cities**: Use IMX500-exported YOLOv8 models for traffic monitoring and safety analysis with faster processing and minimal latency.
***Retail Analytics**: Enhance in-store monitoring by deploying optimized models in point-of-sale systems or smart shelves.
-**Edge AI and IoT**: Enable object detection on drones or security cameras, where real-time processing on low-power devices is essential.
-**Wearable Devices**: Deploy models optimized for small-scale AI processing on health-monitoring wearables.
-**Smart Cities**: Use IMX500-exported YOLOv8 models for traffic monitoring and safety analysis with faster processing and minimal latency.
-**Retail Analytics**: Enhance in-store monitoring by deploying optimized models in point-of-sale systems or smart shelves.
## Conclusion
Exporting Ultralytics YOLOv8 models to Sony's IMX500 format allows you to deploy your models for efficient inference on IMX500-based cameras. By leveraging advanced quantization and pruning techniques, you can reduce model size and improve inference speed without significantly compromising accuracy.
Exporting Ultralytics YOLOv8 models to Sony's IMX500 format allows you to deploy your models for efficient inference on IMX500-based cameras. By leveraging advanced quantization and pruning techniques, you can reduce model size and improve inference speed without significantly compromising accuracy.
For more information and detailed guidelines, refer to Sony's [IMX500 website](https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera).
UltralyticsAssistant
4 months ago