Apprentissage automatique · GitHub

A framework for high-performance medical image processing, neural network inference and visualization

• Data streaming – Processing pipelines in FAST can handle both static and dynamic/temporal data without any change to the code. FAST can stream data from movie files, your webcamera, an Intel RealSense camera, a sequence of images and even directly from ultrasound scanners such as Clarius.
• Deep learning – FAST provides a common interface for neural networks supporting different model formats (ONNX, protobuf, SavedModel, OpenVINO, UFF) and backends (Google TensorFlow, NVIDIA TensorRT, Intel OpenVINO, Microsoft ONNX Runtime), making it possible to create real-time neural network pipelines.

AI Ecosystem Platform 2026: Train Models & Generate Images Seamlessly

An android library that uses technologies like artificial Intelligence, machine learning, and deep learning to make developers understand the content that they are displaying in their app.

An annotation and instance segmentation-based multi-object tracking and behavior analysis package.

• Annotate images and videos - Draw polygons, boxes, keypoints, and zones; save LabelMe-compatible annotations.
• Track multiple animals - Use labeled seed frames with segmentation and tracking models, then review masks and identities.

Clair3 - Symphonizing pileup and full-alignment for deep learning-based long-read variant calling

📸📸📸 Opensource Image Generation AI tools🌟 Star if you like it! 🌟

HFTFramework utilized for research on " A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm "

Autonomous driving neural network comparison tool