Projects sharing features with SLIP

This repository provides the code and model checkpoints for AIMv1 and AIMv2 research projects.

The Satellite Imagery Multiscale Rapid Detection with Windowed Networks (SIMRDWN) codebase combines some of the leading object detection algorithms into a unified framework designed to detect objects both large and small in overhead imagery. This work seeks to extend the YOLT modification of…

YOLTv4 builds upon YOLT and SIMRDWN, and updates these frameworks to use the most performant version of YOLO, YOLOv4. YOLTv4 is designed to detect objects in aerial or satellite imagery in arbitrarily large images that far exceed the ~600×600 pixel size typically ingested by deep learning object…

This repository includes the official project for the paper: TransMix: Attend to Mix for Vision Transformers, CVPR 2022

Darknet is a high-performance C-based inference engine and computer vision library designed for real-time object identification and localization. It serves as a neural network framework for training and deploying detection models using the YOLO architecture, providing a toolset for deep learning training and deployment. The project differentiates itself through a C and CUDA implementation that enables hardware acceleration for matrix multiplication and inference speed optimization. It provides a shared library interface for embedding detection capabilities into external applications and suppo

Ciencia & Datos

R-FCN: Object Detection via Region-based Fully Convolutional Networks

This codebase is created to build benchmarks for object detection in aerial images. It is modified from mmdetection. The master branch works with PyTorch 1.1 or higher. If you would like to use PyTorch 0.4.1, please checkout to the pytorch-0.4.1 branch.

Counting trees manually from satellite images above is a very tedious task. Fortunately, there are several techniques for automating tree counting. Morphological operations and classical segmentation algorithms like the watershed algorithm have been applied to tree counting with limited success…