https://github.com/iammahesh123/image-object-detection-using-opencv-with-machine-learning

https://github.com/iammahesh123/image-object-detection-using-opencv-with-machine-learning

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• Host: GitHub
• URL: https://github.com/iammahesh123/image-object-detection-using-opencv-with-machine-learning
• Owner: iammahesh123
• License: mit
• Created: 2021-07-15T11:11:59.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-15T11:29:46.000Z (about 3 years ago)
• Last Synced: 2023-11-13T19:28:49.848Z (about 1 year ago)
• Language: Python
• Size: 18.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Object-Detection using Machine Learning

OpenCV dnn module supports running inference on pre-trained deep learning models from popular frameworks like Caffe, Torch and TensorFlow.

When it comes to object detection, popular detection frameworks are

YOLO

SSD

Faster R-CNN

Support for running YOLO/DarkNet has been added to OpenCV dnn module recently.


Dependencies

opencv

numpy

pip install numpy opencv-python


YOLO(You Only Look Onces)


Download the pre-trained YOLO v3 weights file from this link.

YOLO algorithm employs convolutional neural networks (CNN) to detect objects in real-time. ... This means that prediction in the entire image is done in a single algorithm run. The CNN is used to predict various class probabilities and bounding boxes simultaneously. The YOLO algorithm consists of various variants.

CNN(Convolutional Neural Network)


A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other.

Sample Output