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Image Classification

Efficient CNN model for image classification on mobile and embedded edge devices.

$0.00
More Information
Image Classification
app details
Model Type

Quantized CNN for Classification

Framework

TensorFlow Lite

Algorithm

ShuffleNet v2 (Quantized)

Technical Details

ShuffleNet v2 is a compact and high-performance convolutional neural network designed for real-time image classification on edge devices. This implementation utilizes a quantized TFLite model to enable efficient deployment on low-resource processors. Ideal for smart IoT devices, vision sensors, and applications requiring responsive classification in constrained environments. The architecture combines group convolution and channel shuffle operations to minimize memory and computation load.

Use Cases
Quality Control
Supported IoT Devices
AI optimized hardware Fanless embedded PCs Modules
supported chipsets
Intel® Core™ i3 Qualcomm® Dragonwing QCS6490 Qualcomm® Dragonwing QCS5430 NXP i.MX 8M Plus NXP i.MX 95
Performance Benchmark
Hardware
Latency (ms)
Memory Usage
Processing unit
SECO SOM-SMARC-QCS6490
3.97 ms
0.36 MB
CPU
SECO SOM-SMARC-iMX8Plus
52.34 ms
0.36 MB
CPU
SECO SOM-SMARC-MX95
19.11 ms
0.36 MB
CPU
SECO Titan 300 TGL-UP3 AI
2.83 ms
0.36 MB
CPU
SECO SOM-SMARC-iMX8Plus
17.93 ms
0.36 MB
NPU

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