INTRODUCTION

YOLOv7 algorithms can be used to recognize and track objects as they move through a production line, allowing for more efficient and accurate manufacturing. Additionally, object detection is used for quality control and defect detection in products or components as they are being manufactured.

DOWNLOAD LINK

Watch On YOUTUBE 😃👇

https://www.youtube.com/watch?v=RExvAXHW7jo

CODE :

from roboflow import Roboflow
rf = Roboflow(api_key="riMiboxeLKguBWpBlc7J")
project = rf.workspace().project("emotion-detection-project-ooyeo")
model = project.version(1).model

# infer on a local image
print(model.predict("a.jpg", confidence=40, overlap=30).json())

# visualize your prediction
model.predict("a.jpg", confidence=40, overlap=30).save("b.jpg")

# infer on an image hosted elsewhere
# print(model.predict("URL_OF_YOUR_IMAGE", hosted=True, confidence=40, overlap=30).json())

FULL CODE WITH DATASET 😃👇

https://tnvalue.in/emotion