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This project is a game automation tool and deep reinforcement learning fishing bot designed to automate the fishing mechanics in Genshin Impact. It functions as an AI gameplay controller that simulates player inputs to collect resources without manual effort.
The main features of irisrainbowneko/genshin_auto_fish are: Gameplay Controllers, Object Detection, Game Automation Bots, Computer Vision, Reinforcement Learning, Computer Vision Projects, Keyboard and Mouse Input Simulations, Game Input Automation.
Projects with overlapping indexed features include: sentdex/pygta5 — pygta5 is a Python game automation library designed to control actions and simulate player input within Grand Theft… dragen1860/tensorflow-2.x-tutorials — This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a… ultralytics/ultralytics — Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep… pytorch/vision — This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection… dmlc/gluon-cv — Gluon-CV is an MXNet computer vision library that provides a comprehensive collection of pre-implemented vision… serpentai/serpentai — SerpentAI is a game AI development kit and computer vision framework designed for building autonomous agents that…
pygta5 is a Python game automation library designed to control actions and simulate player input within Grand Theft Auto 5. It serves as a framework for collecting game data, processing visual frames via neural networks, and automating gameplay through deep learning. The project implements a convolutional neural network controller to make real-time driving and movement predictions based on visual game frames. It utilizes regression models and deep learning to execute autonomous actions, allowing for the creation of autonomous agents that can control characters or vehicles. The system include
This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes
This project is a comprehensive computer vision library for the PyTorch ecosystem, providing a standardized collection of neural network architectures, datasets, and high-performance transformation utilities. It serves as a foundational framework for building, training, and deploying deep learning models, offering a centralized model registry that allows developers to instantiate architectures with pre-trained weights for tasks such as image classification, object detection, and semantic segmentation. The library distinguishes itself through its modular approach to data and compute management
Ultralytics is a comprehensive computer vision framework designed for training, validating, and deploying deep learning models across a wide range of visual recognition tasks. It provides a unified interface for core operations including object detection, instance segmentation, pose estimation, and image classification. By utilizing a modular architecture, the platform allows users to swap model components to balance inference speed and accuracy requirements for diverse applications. The framework distinguishes itself through its support for real-time processing and flexible deployment. It in