Ultralytics YOLO27, YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking
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Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
We write your reusable computer vision tools. 💜
deep learning for image processing including classification and object-detection etc.
Image annotation with Python. Supports polygon, rectangle, circle, line, point, and AI-assisted annotation.
Image augmentation for machine learning experiments.
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Accelerated deep learning R&D
An open source library and framework for deep learning on satellite and aerial imagery.
StarDist - Object Detection with Star-convex Shapes
TensorFlow Implementation for Computing a Semantically Segmented Bird's Eye View (BEV) Image Given the Images of Multiple Vehicle-Mounted Cameras.
Deep Learning Library for Single Cell Analysis
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark Suite
Deep and Machine Learning for Microscopy
PyTorch Connectomics: segmentation toolbox for EM connectomics
A tool for cell instance aware segmentation in densely packed 3D volumetric images
Axon/Myelin segmentation using Deep Learning
RootPainter: Deep Learning Segmentation of Biological Images with Corrective Annotation
Accurate segmentation of bacterial microscope images using deep learning synthetically generated image data.
Panoptic segmentation algorithms for 2D and 3D electron microscopy images