mmdet 3.3.0

Ultralytics YOLO for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification.

Time series forecasting suite using deep learning models

PyTorch Image Models

Neural Networks Compression Framework

Fast and Accurate ML in 3 Lines of Code

Fast and Accurate ML in 3 Lines of Code

State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow

StarDist is a Python package designed for object detection with star-convex shapes, particularly useful in microscopy. It provides implementations for both 2D and 3D images and uses a model trained to predict distances to the object boundary along fixed rays and object probabilities. These predictions produce an overcomplete set of candidate polygons for a given image, with the final result obtained via non-maximum suppression.

StarDist is compatible with Python 3.6 to 3.10, requires TensorFlow, and provides pre-trained models for 2D images and example workflows via Jupyter notebooks, making it a versatile tool for cell detection and segmentation in microscopy.

This is a comprehensive library for generalized Out-of-Distribution (OOD) detection research. It provides over 20 detection methods, evaluation pipelines, OOD datasets, and model architectures integrated with timm. Additionally, it offers fast OOD evaluation metrics, multi-layer detection methods, and pipelines for open set recognition and covariate drift detection.