Models

Explore popular model architectures, pretrained models, and foundation models. You may wish to also check out Libraries: Model exploration for libraries (e.g., MONAI, Kornia) that allow you to implement and test a variety of models with ease.

Pytesseract: OCR with Tesseract (LSTM) in Python

Computer vision

Text extraction

Pytesseract is a Python wrapper for Google’s Tesseract Optical Character Recognition (OCR) engine, used for recognizing and extracting text from images. It works on a wide…

DeepForest: A Toolkit of Models for Tree and Wildlife Detection in Aerial Imagery

Object detection

Forest monitoring

Geospatial data

Computer vision

DeepForest is an open-source Python library for object detection in aerial RGB imagery, designed for ecological applications such as identifying tree crowns (the visible…

XGBoost: Tree-Based Gradient Boosting for Tabular Data

XGBoost, or eXtreme Gradient Boosting, is a machine learning algorithm built upon the foundation of decision trees, extending their power through boosting. Originally…

U-Net: Convolutional Networks for Biomedical Image Segmentation

Medical imaging

Image segmentation

U-Net is a convolutional neural network architecture designed for biomedical image segmentation. Introduced in 2015 by Ronneberger and colleagues in the paper, “U-Net…

MONAI: Medical Open Network for AI

Medical imaging

Model exploration

MONAI (Medical Open Network for AI) is an open-source, community-supported framework for deep learning in healthcare imaging. Built on top of PyTorch, it provides…