#machine learning models

Netron is a multi-device tool for viewing a broad range of machine learning models, supporting formats like ONNX and TensorFlow Lite, with additional experimental framework compatibility. Installable on major platforms, it simplifies the understanding of complex models for developers, researchers, and data scientists.

GAM Changer offers an interactive visualization tool for domain experts and data scientists to edit Generalized Additive Models (GAMs) efficiently. Users can upload and modify their models using an intuitive interface, integrate with Jupyter and Google Colab ecosystems, and maintain comprehensive editing histories. Developed with Microsoft Research and leading academic partners, this tool is available as an easily installable Python package.

Explore the use of Llama 2 via a versatile Gradio interface, adaptable for use with GPUs and CPUs across Linux, Windows, and Mac. The platform accommodates various Llama 2 models in both 8-bit and 4-bit configurations, including CodeLlama, thus broadening AI functionalities for developers. Employ llama2-wrapper to integrate with applications for generative tasks or deploy an OpenAI-compatible API for straightforward model interfacing. With comprehensive documentation and performance benchmarks, this tool is tailored to ensure efficiency across various devices without overstated claims.

Explore an innovative annotation tool that boosts productivity through AI-driven labeling for complex visual data tasks. It supports image and video processing with seamless import/export across COCO, VOC, and YOLO formats. Featuring one-click model inference, custom model support, and GPU acceleration, it accommodates tasks like classification, detection, and segmentation. Its comprehensive model library, including YOLO and Segment Anything, delivers quality outcomes for visual data professionals.

Foolbox is a Python library for executing adversarial attacks on models built with PyTorch, TensorFlow, and JAX. Utilizing EagerPy, it delivers native performance and supports cutting-edge attacks for robust model testing. With extensive type checking and comprehensive documentation, it facilitates easy integration and contribution to its growth. It requires Python 3.8+ and separate framework installations for tailored flexibility.