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A Python Library for Computer-Vision related Tasks

Project description

Xtreme-Vision

Build Status License: MIT

Xtreme-Vision is a Python Library which is built with simplicity in mind for Computer Vision Tasks, such as Object-Detection, Human-Pose-Estimation, Image-Segmentation Tasks, it provides the support of a list of state-of-the-art algorithms, You can Start Detecting with Pretrained Weights as well as You can train the Models On Custom Dataset.

Currently, It Provides the Solution for the following Tasks:

  • Object-Detection
  • Pose-Estimation

For Detection with pre-trained models it provides:

  • RetinaNet
  • CenterNet
  • YOLOv4
  • TinyYOLOv4

For Custom Training It Provides:

  • YOLOv4
  • TinyYOLOv4

In Future it will provide solution for a wide variety of Computer-Vision Tasks such as Object-Detection, Pose-Estimation, Image-Segmentation, Image-Prediction, Auto-Encoders and GANs.

If You Like this Project Please do support it by donating here Build Status

Dependencies:

  • Tensorflow >= 2.3.0
  • Keras
  • Opencv-python
  • Numpy
  • Pillow
  • Matplotlib
  • Pandas
  • Scikit-learn
  • Progressbar2
  • Scipy
  • H5Py

Get Started:

!pip install xtreme-vision

For More Tutorials of Xtreme-Vision, Click Here

RetinaNet Example

Image Object_Detection Using RetinaNet

from xtreme_vision.Detection import Object_Detection

model = Object_Detection()
model.Use_RetinaNet()
model.Detect_From_Image(input_path='kite.jpg',
                        output_path='./retinanet.jpg', 
                        extract_objects=True)

from PIL import Image
Image.open('retinanet.jpg')

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