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An open-source application for biological image analysis
The main features of cellprofiler/cellprofiler are: Computer Vision, Scientific Research Tools, Analysis Pipelines.
Projects with overlapping indexed features include: 3b1b/manim — Manim is a Python-based computational geometry framework designed for programmatic video production. It functions as a… achaiah/pywick — High-level batteries-included neural network training library for Pytorch. ailab-cvc/yolo-world — YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images… airctic/icevision — An Agnostic Computer Vision Framework - Pluggable to any Training Library: Fastai, Pytorch-Lightning with more to come. alankbi/detecto — Build fully-functioning computer vision models with PyTorch. 1adrianb/face-alignment — This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a…
Manim is a Python-based computational geometry framework designed for programmatic video production. It functions as a mathematical animation engine, allowing users to generate high-fidelity visual content by scripting scene definitions rather than using traditional timeline-based editing software. The library is built to translate code-based instructions into precise, frame-accurate animations, making it a tool for explaining complex mathematical functions, geometric proofs, and abstract theories. The engine distinguishes itself through a declarative scene graph that organizes visual element
High-level batteries-included neural network training library for Pytorch
YOLO-World is a vision-language framework and open-vocabulary object detection model. It identifies objects in images and video based on free-form text prompts without requiring predefined category labels. The system enables the identification of arbitrary objects by fusing image features with text embeddings. It includes a specialized tool for automated image labeling, which generates bounding box annotations for custom datasets using text-based prompts. The project provides a deployment pipeline for converting models into quantized ONNX and TFLite formats, supporting real-time inference on
This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p