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.

MegaDetector: Pre-Trained Animal Object Detection for Camera Trap Images

Computer vision

Object detection

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is…

Pytesseract: OCR with Tesseract (LSTM) in Python

Computer vision

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

Model exploration

Object detection

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…