OpenMMLab Detection Toolbox and Benchmark
翻译 - OpenMMLab检测工具箱和基准
#计算机科学#We write your reusable computer vision tools. 💜
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow
翻译 - 遮罩R-CNN，用于在Keras和TensorFlow上进行对象检测和实例分割
#计算机科学#Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
翻译 - 使用Python的图像多边形注释（多边形，矩形，圆形，直线，点和图像级标记注释）。
#人脸识别#PaddleDetection为基于飞桨PaddlePaddle的端到端目标检测套件，提供多种主流目标检测、实例分割、跟踪、关键点检测算法，配置化的网络模块组件、数据增强策略、损失函数等，推出多种服务器端和移动端工业级SOTA模型，并集成了模型压缩和跨平台高性能部署能力,帮助开发者更快更好完成端到端全开发流程。
A simple, fully convolutional model for real-time instance segmentation.
翻译 - 一个简单的全卷积模型，用于实时实例分割。
#计算机科学#Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
翻译 - 用于执行大规模对象检测/实例分割的轻量级视觉库。
#计算机科学#🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
AdelaiDet is an open source toolbox for multiple instance-level detection and recognition tasks.
翻译 - AdelaiDet是一个开源工具箱，用于执行多个实例级检测和识别任务。
🔥🔥🔥🔥 (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! 🔥🔥🔥
翻译 - 🔥🔥🔥🔥 带有 Transformers 和 Instance Segmentation 的 YOLO，带有 TensorRT 加速！ 🔥🔥🔥
A Simple and Versatile Framework for Object Detection and Instance Recognition
翻译 - 用于对象检测和实例识别的简单通用框架
#计算机科学#Unofficial implemention of lanenet model for real time lane detection
翻译 - 使用深度神经网络模型进行实时车道检测的lenet模型的非官方实现https://maybeshewill-cv.github.io/lanenet-lane-detection/
#计算机科学#Images to inference with no labeling (use foundation models to train supervised models).
SOLO and SOLOv2 for instance segmentation, ECCV 2020 & NeurIPS 2020.
翻译 - 例如SOLO和SOLOv2细分，ECCV 2020和NeurIPS 2020。
🔥[IEEE TPAMI 2020] Deep Learning for 3D Point Clouds: A Survey
[CVPR 2023] OneFormer: One Transformer to Rule Universal Image Segmentation
#计算机科学#Turn any computer or edge device into a command center for your computer vision projects.
PANet for Instance Segmentation and Object Detection
#计算机科学#[ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling...