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A collection of machine-learned models for use in autonomous driving applications.
The main features of autowarefoundation/modelzoo are: Machine Learning Frameworks, Perception and Machine Learning.
Projects with overlapping indexed features include: davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… epistasislab/tpot — TPOT is a Python automated machine learning tool and pipeline framework. It automatically searches, selects, and tunes… alibaba/mnn — MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a… catalyst-team/catalyst — Accelerated deep learning R&D. deap/deap. facebookresearch/reagent — ReAgent is a reinforcement learning platform designed for training, deploying, and evaluating reinforcement learning…
dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and utilities for building predictive modeling applications and performing statistical analysis on large datasets within native C++ environments. The project functions as a binding library that wraps low-level C++ machine learning algorithms into high-level Python scripting interfaces. This allows for the integration of high-performance native implementations with Python for machine learning development. The framework covers the implementation of predictive models, the execution of mach
MNN is a high-performance inference engine and framework designed for on-device machine learning. It provides a comprehensive environment for executing, optimizing, and deploying neural network models directly on mobile and resource-constrained edge devices. The framework distinguishes itself through a robust model optimization toolkit that supports quantization, compression, and structural graph manipulation to minimize memory footprint and maximize execution speed. It features a modular architecture that abstracts hardware-specific backends, allowing models to run efficiently across diverse
