A High Level Python Library to empower students, developers to build applications and systems enabled with computer vision capabilities.

Xtreme-Vision

Go to PyPI page> Here

This is the Official Repository of Xtreme-Vision. Xtreme-Vision is a High Level Python Library which is built with simplicity in mind for Computer Vision Tasks, such as Object-Detection, Human-Pose-Estimation, 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 and with Xtreme-Vision you have the Power to detect/segment only the Objects of your interest

Currently, It Provides the Solution for the following Tasks:

• Object-Detection
• Pose-Estimation
• Segmentation

For Detection with pre-trained models it provides:

• RetinaNet
• CenterNet
• YOLOv4
• TinyYOLOv4
• Mask-RCNN
• DeepLabv3+

For Custom Training It Provides:

• YOLOv4
• TinyYOLOv4
• RetinaNet with (resnet50, resnet101, resnet152)

In Future it will provide solution for a wide variety of Computer-Vision Tasks such as Object-Detection, Pose-Estimation, Object Segmentation, Image-Prediction, Auto-Encoders and GANs with 2D as well as 3D Models and it will support More State-Of-the-Art Algorithms.

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

Dependencies:

• tensorflow >= 2.3.0
• keras
• opencv-python
• numpy
• pillow
• matplotlib
• pandas
• scikit-learn
• scikit-image
• imgaug
• labelme2coco
• progressbar2
• scipy
• h5py

Get Started:

!pip install xtreme-vision

For More Tutorials of Xtreme-Vision, Click Here

YOLOv4 Example

Image Object Detection Using YOLOv4

from xtreme_vision.Detection import Object_Detection

model = Object_Detection()
model.Use_YOLOv4()
model.Detect_From_Image(input_path='kite.jpg',
                        output_path='./output.jpg')

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