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Google AIY Vision Complete Kit - V1.1
The Google AIY Vision Complete Kit V1.1 is a ready-to-assemble artificial intelligence development platform that enables machine learning-based computer vision applications on edge devices using TensorFlow Lite. This kit is extensively used by embedded systems engineers, roboticists, and IoT developers who need to deploy real-time image recognition and object detection models without relying on cloud infrastructure. It solves the critical problem of implementing intelligent visual perception on resource-constrained hardware while maintaining low latency and offline operation capabilities.
Product Overview
The Google AIY Vision Kit V1.1 integrates a Raspberry Pi Zero WH microcomputer with a custom Vision Bonnet accelerator that features a Movidius Myriad X Vision Processing Unit (VPU). This hardware combination enables efficient inference of pre-trained neural network models optimized for edge deployment. The Vision Bonnet communicates with the Raspberry Pi via the 40-pin GPIO header, providing dedicated hardware acceleration for computer vision tasks while consuming minimal power. The kit comes pre-loaded with TensorFlow Lite models that support object detection, image classification, and pose estimation, allowing developers to deploy production-grade AI applications with inference speeds reaching 30+ frames per second on 1080p video streams.
What distinguishes the V1.1 revision is its improved thermal management, enhanced driver stability, and compatibility with the latest TensorFlow Lite runtime environments. The Movidius Myriad X VPU delivers approximately 4 TOPS (Tera Operations Per Second) of performance, making it capable of executing complex deep learning models with quantized weights and activations. The complete kit includes the Raspberry Pi Zero WH with pre-soldered GPIO headers, the Vision Bonnet with integrated microphone and speaker connectors, a 5MP camera module, comprehensive documentation, and example Python code for rapid prototyping. This architecture eliminates the need for GPU-based cloud processing, enabling privacy-preserving machine learning inference at the edge.
Key Specifications
| Specification | Details |
| Product Type | AI Vision Development Kit with Edge Accelerator |
| Brand | |
| Model | AIY Vision Complete Kit V1.1 |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processor | Raspberry Pi Zero WH with BCM2835 ARM11 1GHz CPU |
| Vision Accelerator | Movidius Myriad X VPU with 4 TOPS Performance |
| RAM | 512MB LPDDR2 SDRAM |
| Storage | microSD Card Slot (up to 32GB recommended) |
| Camera Module | 5MP OV5647 CMOS Sensor with Fixed Focus Lens |
| Video Output | Mini HDMI, USB-A via OTG Adapter |
| Power Consumption | 2.5W typical, 4W peak with camera active |
| Operating Temperature | 0 to 50 degrees Celsius |
| Supported Frameworks | TensorFlow Lite, OpenVINO Toolkit |
Key Features
- Movidius Myriad X Neural Processing Unit delivering 4 TOPS of dedicated AI inference acceleration for real-time computer vision tasks without GPU overhead
- Pre-installed TensorFlow Lite models including MobileNet for object detection, PoseNet for pose estimation, and custom quantized neural networks optimized for edge deployment
- Integrated 5MP camera module with 160-degree field of view enabling capture of high-quality video streams for machine learning inference at 30+ FPS on 1080p resolution
- Low-power ARM-based Raspberry Pi Zero WH processor consuming only 2.5W during typical operation, ideal for battery-powered and IoT applications requiring extended runtime
- Complete Python SDK with pre-written examples and comprehensive documentation enabling rapid prototyping and deployment of custom vision models within hours
- Privacy-preserving offline inference capability eliminating cloud connectivity requirements and ensuring sensitive visual data remains on-device for security-critical applications
Applications and Use Cases
- Smart Surveillance Systems: Deploy real-time object detection and person counting for security monitoring with on-device processing, reducing bandwidth requirements and ensuring privacy compliance in sensitive environments
- Industrial Quality Control: Implement automated visual inspection systems that detect manufacturing defects, component misalignment, and quality anomalies with sub-second latency on production lines
- Robotics and Autonomous Navigation: Enable mobile robots and autonomous vehicles to perform real-time obstacle detection, path planning, and gesture recognition using edge-based computer vision inference
- Agricultural Monitoring: Deploy crop health assessment systems that analyze plant diseases, pest infestations, and growth patterns through image classification models running on edge hardware in remote farm locations
- Retail Analytics: Create customer behavior analysis systems that track foot traffic patterns, product shelf interactions, and demographic insights without cloud dependency or privacy concerns
- Environmental Monitoring: Build wildlife detection and species classification systems for conservation projects using battery-powered edge devices capable of operating in remote locations for extended periods
How to Use
Begin by assembling the hardware components: insert the pre-soldered Raspberry Pi Zero WH into the Vision Bonnet's 40-pin GPIO connector, ensuring proper alignment and secure seating. Connect the 5MP camera module to the CSI ribbon connector on the Raspberry Pi, taking care to lift the connector clip gently before inserting the ribbon with the blue side facing outward. Flash a microSD card with the latest Raspberry Pi OS or Google's custom AIY image using Balena Etcher or similar tool, then insert the card into the microSD slot. Power the device using a 5V 2.5A USB-C power adapter, and connect via SSH or the desktop environment to access the Python development environment.
Install the AIY Python library using pip with the command: pip3 install google-aiy. Verify camera functionality by running the provided test scripts that capture images and display inference results. For custom model deployment, convert your TensorFlow models to TensorFlow Lite format using the official conversion tools, ensuring quantization for optimal performance on the Myriad X accelerator. Use the provided examples in /home/pi/AIY-projects-python/src/examples/ as templates for implementing object detection, image classification, or pose estimation pipelines. Test inference latency and accuracy using sample images before deploying to production, and monitor CPU and VPU utilization using the included monitoring tools to optimize model performance.
Frequently Asked Questions
What is the inference latency for object detection models on the Myriad X VPU?
The Movidius Myriad X VPU achieves inference latency of 30-50 milliseconds for quantized MobileNet-based object detection models on 1080p video streams, enabling real-time processing at 20-30 frames per second. Actual latency depends on model complexity, input resolution, and quantization precision. Lighter models like MobileNetV2 achieve sub-30ms latency, while larger models may require frame skipping or resolution reduction for real-time performance.
Can I deploy custom TensorFlow models on the AIY Vision Kit V1.1?
Yes, you can deploy custom models by converting them to TensorFlow Lite format with quantization optimization. The Myriad X VPU supports INT8 quantized models most efficiently. Use the TensorFlow Lite converter with post-training quantization to reduce model size and improve inference speed. Ensure your model architecture is compatible with edge deployment constraints, typically requiring models with fewer than 100MB parameters for optimal performance on the 512MB RAM available on Raspberry Pi Zero WH.
What power supply specifications are required for stable operation?
The AIY Vision Kit requires a 5V USB-C power adapter with minimum 2.5A output capacity. Under peak load with camera active and Myriad X accelerator running inference, the system draws approximately 4W (800mA at 5V). Using inadequate power supplies causes voltage drops leading to system instability, random reboots, and inference errors. We recommend using quality power adapters rated for at least 3A to provide sufficient headroom for stable operation during intensive computer vision workloads.
Is the Raspberry Pi Zero WH in the kit pre-configured with WiFi and Bluetooth?
The Raspberry Pi Zero WH included in the AIY Vision Kit does not include built-in WiFi or Bluetooth connectivity. The Zero WH variant features only pre-soldered GPIO headers. For wireless connectivity, you must add a USB WiFi dongle or Bluetooth adapter via the micro-USB OTG port. Alternatively, use Ethernet connectivity through a USB-to-Ethernet adapter for network access and remote SSH connections to the device.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India. Express shipping options may be available for urgent requirements. You will receive tracking information via email and SMS once your order is dispatched.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse. The warranty covers hardware defects in the Raspberry Pi Zero WH, Vision Bonnet, and camera module. Damage from improper power supply, static discharge, or physical damage is not covered under warranty.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote. We offer significant discounts for educational institutions, research organizations, and commercial deployments requiring multiple kits.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication certificates and batch verification
- Expert Team: Our technical team validates every product before listing and provides pre-sales consultation for project requirements
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse with real-time tracking updates
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available for orders above specified amounts
- Technical Support: 24/7 expert guidance via email and WhatsApp from engineers experienced with AIY Vision Kit deployments
- Returns:
Buy Google AIY Vision Complete Kit - V1.1 Online in India
Purchase the Google AIY Vision Complete Kit - V1.1 online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.
Google AIY Vision Complete Kit - V1.1
The Google AIY Vision Complete Kit V1.1 is a ready-to-assemble artificial intelligence development platform that enables machine learning-based computer vision applications on edge devices using TensorFlow Lite. This kit is extensively used by embedded systems engineers, roboticists, and IoT developers who need to deploy real-time image recognition and object detection models without relying on cloud infrastructure. It solves the critical problem of implementing intelligent visual perception on resource-constrained hardware while maintaining low latency and offline operation capabilities.
Product Overview
The Google AIY Vision Kit V1.1 integrates a Raspberry Pi Zero WH microcomputer with a custom Vision Bonnet accelerator that features a Movidius Myriad X Vision Processing Unit (VPU). This hardware combination enables efficient inference of pre-trained neural network models optimized for edge deployment. The Vision Bonnet communicates with the Raspberry Pi via the 40-pin GPIO header, providing dedicated hardware acceleration for computer vision tasks while consuming minimal power. The kit comes pre-loaded with TensorFlow Lite models that support object detection, image classification, and pose estimation, allowing developers to deploy production-grade AI applications with inference speeds reaching 30+ frames per second on 1080p video streams.
What distinguishes the V1.1 revision is its improved thermal management, enhanced driver stability, and compatibility with the latest TensorFlow Lite runtime environments. The Movidius Myriad X VPU delivers approximately 4 TOPS (Tera Operations Per Second) of performance, making it capable of executing complex deep learning models with quantized weights and activations. The complete kit includes the Raspberry Pi Zero WH with pre-soldered GPIO headers, the Vision Bonnet with integrated microphone and speaker connectors, a 5MP camera module, comprehensive documentation, and example Python code for rapid prototyping. This architecture eliminates the need for GPU-based cloud processing, enabling privacy-preserving machine learning inference at the edge.
Key Specifications
| Specification | Details |
| Product Type | AI Vision Development Kit with Edge Accelerator |
| Brand | |
| Model | AIY Vision Complete Kit V1.1 |
| Origin | Original/Authentic |
| Warranty | 7 days on manufacturing defects |
| Shipping | 1-5 days from Bengaluru |
| Delivery | 7-8 days across India |
| Support | 24/7 via Email and WhatsApp |
| Processor | Raspberry Pi Zero WH with BCM2835 ARM11 1GHz CPU |
| Vision Accelerator | Movidius Myriad X VPU with 4 TOPS Performance |
| RAM | 512MB LPDDR2 SDRAM |
| Storage | microSD Card Slot (up to 32GB recommended) |
| Camera Module | 5MP OV5647 CMOS Sensor with Fixed Focus Lens |
| Video Output | Mini HDMI, USB-A via OTG Adapter |
| Power Consumption | 2.5W typical, 4W peak with camera active |
| Operating Temperature | 0 to 50 degrees Celsius |
| Supported Frameworks | TensorFlow Lite, OpenVINO Toolkit |
Key Features
- Movidius Myriad X Neural Processing Unit delivering 4 TOPS of dedicated AI inference acceleration for real-time computer vision tasks without GPU overhead
- Pre-installed TensorFlow Lite models including MobileNet for object detection, PoseNet for pose estimation, and custom quantized neural networks optimized for edge deployment
- Integrated 5MP camera module with 160-degree field of view enabling capture of high-quality video streams for machine learning inference at 30+ FPS on 1080p resolution
- Low-power ARM-based Raspberry Pi Zero WH processor consuming only 2.5W during typical operation, ideal for battery-powered and IoT applications requiring extended runtime
- Complete Python SDK with pre-written examples and comprehensive documentation enabling rapid prototyping and deployment of custom vision models within hours
- Privacy-preserving offline inference capability eliminating cloud connectivity requirements and ensuring sensitive visual data remains on-device for security-critical applications
Applications and Use Cases
- Smart Surveillance Systems: Deploy real-time object detection and person counting for security monitoring with on-device processing, reducing bandwidth requirements and ensuring privacy compliance in sensitive environments
- Industrial Quality Control: Implement automated visual inspection systems that detect manufacturing defects, component misalignment, and quality anomalies with sub-second latency on production lines
- Robotics and Autonomous Navigation: Enable mobile robots and autonomous vehicles to perform real-time obstacle detection, path planning, and gesture recognition using edge-based computer vision inference
- Agricultural Monitoring: Deploy crop health assessment systems that analyze plant diseases, pest infestations, and growth patterns through image classification models running on edge hardware in remote farm locations
- Retail Analytics: Create customer behavior analysis systems that track foot traffic patterns, product shelf interactions, and demographic insights without cloud dependency or privacy concerns
- Environmental Monitoring: Build wildlife detection and species classification systems for conservation projects using battery-powered edge devices capable of operating in remote locations for extended periods
How to Use
Begin by assembling the hardware components: insert the pre-soldered Raspberry Pi Zero WH into the Vision Bonnet's 40-pin GPIO connector, ensuring proper alignment and secure seating. Connect the 5MP camera module to the CSI ribbon connector on the Raspberry Pi, taking care to lift the connector clip gently before inserting the ribbon with the blue side facing outward. Flash a microSD card with the latest Raspberry Pi OS or Google's custom AIY image using Balena Etcher or similar tool, then insert the card into the microSD slot. Power the device using a 5V 2.5A USB-C power adapter, and connect via SSH or the desktop environment to access the Python development environment.
Install the AIY Python library using pip with the command: pip3 install google-aiy. Verify camera functionality by running the provided test scripts that capture images and display inference results. For custom model deployment, convert your TensorFlow models to TensorFlow Lite format using the official conversion tools, ensuring quantization for optimal performance on the Myriad X accelerator. Use the provided examples in /home/pi/AIY-projects-python/src/examples/ as templates for implementing object detection, image classification, or pose estimation pipelines. Test inference latency and accuracy using sample images before deploying to production, and monitor CPU and VPU utilization using the included monitoring tools to optimize model performance.
Frequently Asked Questions
What is the inference latency for object detection models on the Myriad X VPU?
The Movidius Myriad X VPU achieves inference latency of 30-50 milliseconds for quantized MobileNet-based object detection models on 1080p video streams, enabling real-time processing at 20-30 frames per second. Actual latency depends on model complexity, input resolution, and quantization precision. Lighter models like MobileNetV2 achieve sub-30ms latency, while larger models may require frame skipping or resolution reduction for real-time performance.
Can I deploy custom TensorFlow models on the AIY Vision Kit V1.1?
Yes, you can deploy custom models by converting them to TensorFlow Lite format with quantization optimization. The Myriad X VPU supports INT8 quantized models most efficiently. Use the TensorFlow Lite converter with post-training quantization to reduce model size and improve inference speed. Ensure your model architecture is compatible with edge deployment constraints, typically requiring models with fewer than 100MB parameters for optimal performance on the 512MB RAM available on Raspberry Pi Zero WH.
What power supply specifications are required for stable operation?
The AIY Vision Kit requires a 5V USB-C power adapter with minimum 2.5A output capacity. Under peak load with camera active and Myriad X accelerator running inference, the system draws approximately 4W (800mA at 5V). Using inadequate power supplies causes voltage drops leading to system instability, random reboots, and inference errors. We recommend using quality power adapters rated for at least 3A to provide sufficient headroom for stable operation during intensive computer vision workloads.
Is the Raspberry Pi Zero WH in the kit pre-configured with WiFi and Bluetooth?
The Raspberry Pi Zero WH included in the AIY Vision Kit does not include built-in WiFi or Bluetooth connectivity. The Zero WH variant features only pre-soldered GPIO headers. For wireless connectivity, you must add a USB WiFi dongle or Bluetooth adapter via the micro-USB OTG port. Alternatively, use Ethernet connectivity through a USB-to-Ethernet adapter for network access and remote SSH connections to the device.
When will I receive my order?
Orders are dispatched within 1-5 business days from our Bengaluru warehouse. Delivery takes 7-8 days to most locations across India. Express shipping options may be available for urgent requirements. You will receive tracking information via email and SMS once your order is dispatched.
What is your return and warranty policy?
We offer a 7-day return policy on manufacturing defects only. Contact support within 7 days of receipt for free replacement or full refund. Not applicable for user damage or misuse. The warranty covers hardware defects in the Raspberry Pi Zero WH, Vision Bonnet, and camera module. Damage from improper power supply, static discharge, or physical damage is not covered under warranty.
Are bulk discounts available?
Yes, wholesale pricing for orders of 10 or more units. Contact our sales team via WhatsApp or email for a customized bulk quote. We offer significant discounts for educational institutions, research organizations, and commercial deployments requiring multiple kits.
Why Buy from The Engineer Store
- Genuine Products: Sourced directly from authorized distributors with authentication certificates and batch verification
- Expert Team: Our technical team validates every product before listing and provides pre-sales consultation for project requirements
- Fast Shipping: Dispatched within 1-5 days from our Bengaluru warehouse with real-time tracking updates
- Pan-India Delivery: 7-8 days to Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat
- Payment Options: COD, UPI, credit/debit cards, net banking, EMI available for orders above specified amounts
- Technical Support: 24/7 expert guidance via email and WhatsApp from engineers experienced with AIY Vision Kit deployments
- Returns:
Buy Google AIY Vision Complete Kit - V1.1 Online in India
Purchase the Google AIY Vision Complete Kit - V1.1 online at The Engineer Store, India's trusted source for genuine electronics. We deliver across Bengaluru, Mumbai, Delhi, Chennai, Hyderabad, Pune, Kolkata, Ahmedabad, Jaipur, and Surat.
Our team in Bengaluru is available 24/7 to support your journey from product selection to project completion.