YOLOv8-Detection-Quantized
Quantized real-time object detection optimized for mobile and edge by Ultralytics.
Ultralytics YOLOv8 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.
Technical Details
Model checkpoint:YOLOv8-N
Input resolution:640x640
Number of parameters:3.18M
Model size:3.26 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 IoT Devices
- QCS6490 (Proxy)
- QCS8250 (Proxy)
- QCS8550 (Proxy)
- RB3 Gen 2 (Proxy)
- RB5 (Proxy)
Supported IoT Chipsets
- Qualcomm® QCS6490
- Qualcomm® QCS8250
- Qualcomm® QCS8550