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AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
The main features of project-agml/agml are: Machine Learning and AI.
Projects with overlapping indexed features include: aigamedev/btsk — Behavior Tree Starter Kit. affaan-m/ecc — ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model… annetgpgpu/annetgpgpu — A GPU (CUDA) based Artificial Neural Network library. apache/incubator-mxnet — Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying… bvlc/caffe — Caffe is a high-performance deep learning framework designed for training and deploying deep neural networks. It… codeplea/genann — simple neural network library in ANSI C.
ECC is an LLM agent orchestration framework and cross-platform AI tooling suite designed to coordinate multi-model workflows. It provides a system for managing specialized agent roles, reusable skills, and structured planning to execute complex software development tasks across different AI-powered code editors. The project distinguishes itself as a Model Context Protocol manager, providing a configuration layer to integrate external servers and audit tool execution. It further implements an agentic security sandbox that restricts sensitive file access and scans for secret leakage to secure a
A GPU (CUDA) based Artificial Neural Network library
Apache MXNet is a deep learning framework and distributed machine learning library designed for training and deploying neural networks across distributed systems, mobile devices, and hardware accelerators. It functions as a cross-platform runtime and a dynamic dataflow scheduler that optimizes neural network execution. The framework provides a multi-language API, enabling the development of machine learning models using Python, R, Julia, Scala, Go, and JavaScript. It supports high-performance model training and the scaling of workloads across multiple GPUs and machines. The system covers cap
