pycocotools

alfred-py: A deep learning utility library for **human**, more detail about the usage of lib to: https://zhuanlan.zhihu.com/p/341446046

An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come

Continuation of an abandoned project fast-coco-eval

IceData: Datasets Hub for the *IceVision* Framework

Fast alternative to FiftyOne for creating a subset of the COCO dataset.

Clone of COCO API - Dataset @ http://cocodataset.org/ - with changes to support Windows build and python3

This repo contains the process of getting started with Facebook FAIR's detectron2 project on windows 10 without any Nvidia GPU.

Perception evaluation toolkit in Rust with Python bindings. A pycocotools drop-in for COCO, LVIS, and Open Images detection metrics, up to 36× faster, plus TIDE error analysis, confusion matrices, calibration, model comparison, and a dataset browser.

基于 torchvision 编写的针对 COCO 数据集的 Faster-RCNN 快速训练与推理框架，支持多 batchsize。

Simplify COCO dataset management with an intuitive GUI for visualization, merging, splitting, and updating. Perfect for ML and computer vision projects.

This repository contains the code used to train and test a Mask R-CNN model for instance segmentation of transmission towers and electrical cables. We also implemented classic data augmentation techniques and a new method introduced in the paper "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation".

图像标注格式转换器，能让你自由转换YOLO、COCO、VOC格式的图像标注文件

Fast COCO evaluation in Rust with Python compatibility and reproducible public benchmarks

Unofficial APIs for the MS-COCO dataset using PyTorch

MSCOCO data format details and how to evaluate mAP with pycocotools

Install and troubleshoot Alfred 5 on Windows with this configuration guide for setup, updates, and app performance issues.

Uses a CNN Encoder and a RNN Decoder to generate captions for input images.

🌊 Everything your model doesn't do. Analyse, convert, visualize, evaluate — a single CLI for all CV data.

Computer Vision Nanodegree, Udacity, Project_2

Python script generates colored masks from COCO-style annotations. It reads the COCO annotation files, creates masks for each annotation, colors the masks based on the annotation's category, and saves the colored masks as images. The script is designed to handle multiple categories and can be easily extended to support more categories if needed.