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Yolo-NAS-Quantized

Quantized real-time object detection optimized for mobile and edge.

YoloNAS is a machine learning model that predicts bounding boxes and classes of objects in an image. This model is post-training quantized to int8 using samples from the COCO dataset.

TorchScripttoTFLite
5.00ms
Inference Time
0-61MB
Memory Usage
200NPU
3CPU
Layers

Technical Details

Model checkpoint:YoloNAS Small
Input resolution:640x640
Number of parameters:12.2M
Model size:12.1 MB

Applicable Scenarios

  • Factory Automation
  • Robotic Navigation
  • Camera

Supported Form Factors

  • Phone
  • Tablet
  • IoT
  • XR

Licenses

Source Model:APACHE-2.0
Deployable Model:AI Model Hub License

Tags

  • real-time
    A “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.
  • quantized
    A “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.

Supported Devices

  • Google Pixel 3
  • Google Pixel 3a
  • Google Pixel 3a XL
  • Google Pixel 4
  • Google Pixel 4a
  • Google Pixel 5a 5G
  • QCS6490 (Proxy)
  • QCS8250 (Proxy)
  • QCS8550 (Proxy)
  • RB3 Gen 2 (Proxy)
  • RB5 (Proxy)
  • Samsung Galaxy S21
  • Samsung Galaxy S21 Ultra
  • Samsung Galaxy S21+
  • Samsung Galaxy S22 5G
  • Samsung Galaxy S22 Ultra 5G
  • Samsung Galaxy S22+ 5G
  • Samsung Galaxy S23
  • Samsung Galaxy S23 Ultra
  • Samsung Galaxy S23+
  • Samsung Galaxy S24
  • Samsung Galaxy S24 Ultra
  • Samsung Galaxy S24+
  • Samsung Galaxy Tab S8
  • Xiaomi 12
  • Xiaomi 12 Pro

Supported Chipsets

  • Qualcomm® QCS6490
  • Qualcomm® QCS8250
  • Qualcomm® QCS8550
  • Snapdragon® 8 Gen 1 Mobile
  • Snapdragon® 8 Gen 2 Mobile
  • Snapdragon® 8 Gen 3 Mobile
  • Snapdragon® 888 Mobile
  • Snapdragon® X Elite