Open-source alternatives to Simrdwn

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…

Visit PixelLib's official documentation https://pixellib.readthedocs.io/en/latest/

Ciencia & Datos

CosmiQ Works Geospatial Machine Learning Analysis Toolkit

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

Data Preparation for Satellite Machine Learning

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.

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

Detectron is a PyTorch object detection framework and computer vision research platform. It provides implementations of neural network architectures for locating and identifying objects in images, including Mask R-CNN for generating instance segmentation masks and RetinaNet for one-stage detection. The platform supports computer vision prototyping and object detection research through the deployment of pre-trained baseline models. This allows for the rapid implementation and evaluation of visual recognition systems. Its capabilities cover image object localization and instance segmentation w

Detectron2 is a PyTorch computer vision framework and visual recognition platform designed for training and deploying models for object detection, image segmentation, and visual recognition. It provides a research-oriented environment for training complex vision models with multi-GPU acceleration. The project includes a specialized object detection library for identifying and locating multiple objects via bounding boxes, as well as an image segmentation toolkit for creating pixel-level masks through instance, semantic, and panoptic segmentation. Additionally, it features a human pose estimati

Detic: A Detector with image classes that can use image-level labels to easily train detectors.

This project provides a transformer-based object detection model that treats the task as a direct set prediction problem. It implements a vision system capable of predicting bounding boxes and class labels for objects within an image, as well as frameworks for instance and panoptic segmentation. The architecture utilizes a transformer encoder and decoder to perform end-to-end set prediction, employing a Hungarian matcher to assign predicted boxes to ground truth objects. It incorporates a convolutional backbone for feature extraction and a system of learnable object queries to probe image loc

Code release for SLIP Self-supervision meets Language-Image Pre-training

This repository contains the code for the paper ReCLIP: A Strong Zero-shot Baseline for Referring Expression Comprehension (ACL 2022).