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.
Not supported
This model is currently not supported on any Compute chipset.
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View for other chipsetsTechnical 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
Tags
- real-timeA “real-time” model can typically achieve 5-60 predictions per second. This translates to latency ranging up to 200 ms per prediction.
- quantizedA “quantized” model can run in low or mixed precision, which can substantially reduce inference latency.
Supported Compute Chipsets
- Snapdragon® X Elite