HomeIoT ModelsYolo-v7-Quantized

Yolo-v7-Quantized

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

YoloV7 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
4.60ms
Inference Time
0-2MB
Memory Usage
225NPU
1CPU
Layers

Technical Details

Model checkpoint:YoloV7 Tiny
Input resolution:720p (720x1280)
Number of parameters:6.24M
Model size:6.23 MB

Applicable Scenarios

  • Factory Automation
  • Robotic Navigation
  • Camera

Licenses

Source Model:GPL-3.0
Deployable Model:GPL-3.0

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 IoT Devices

  • QCS6490 (Proxy)
  • QCS8250 (Proxy)
  • QCS8550 (Proxy)
  • RB3 Gen 2 (Proxy)
  • RB5 (Proxy)

Supported IoT Chipsets

  • Qualcomm® QCS6490
  • Qualcomm® QCS8250
  • Qualcomm® QCS8550