AI tools for yolo 自瞄
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Ultralytics YOLO
Ultralytics YOLO is an advanced real-time object detection and image segmentation model that leverages cutting-edge advancements in deep learning and computer vision. It offers unparalleled performance in terms of speed and accuracy, making it suitable for various applications and easily adaptable to different hardware platforms. The comprehensive Ultralytics Docs provide resources to help users understand and utilize its features and capabilities, catering to both seasoned machine learning practitioners and newcomers to the field.
Ultralytics
Ultralytics is an AI tool that revolutionizes the world of Vision AI by enabling users to easily turn images into AI to get useful insights without writing any code. It offers a drag-and-drop interface for data input, model training, and deployment, making it accessible for startups, enterprises, data scientists, ML engineers, hobbyists, researchers, and academics. Ultralytics YOLO, the flagship tool, allows users to train machine learning models in seconds, select from pre-built models, test models on mobile devices, and deploy custom models to various formats. The tool is powered by Ultralytics Python package and is open-source, with a focus on computer vision, object detection, and image classification.
llm2sh
llm2sh is a command-line utility that leverages Large Language Models (LLMs) to translate plain-language requests into shell commands. It provides a convenient way to interact with your system using natural language. The tool supports multiple LLMs for command generation, offers a customizable configuration file, YOLO mode for running commands without confirmation, and is easily extensible with new LLMs and system prompts. Users can set up API keys for OpenAI, Claude, Groq, and Cerebras to use the tool effectively. llm2sh does not store user data or command history, and it does not record or send telemetry by itself, but the LLM APIs may collect and store requests and responses for their purposes.
FunGen-AI-Powered-Funscript-Generator
FunGen is a Python-based tool that uses AI to generate Funscript files from VR and 2D POV videos. It enables fully automated funscript creation for individual scenes or entire folders of videos. The tool includes features like automatic system scaling support, quick installation guides for Windows, Linux, and macOS, manual installation instructions, NVIDIA GPU setup, AMD GPU acceleration, YOLO model download, GUI settings, GitHub token setup, command-line usage, modular systems for funscript filtering and motion tracking, performance and parallel processing tips, and more. The project is still in early development stages and is not intended for commercial use.
datadreamer
DataDreamer is an advanced toolkit designed to facilitate the development of edge AI models by enabling synthetic data generation, knowledge extraction from pre-trained models, and creation of efficient and potent models. It eliminates the need for extensive datasets by generating synthetic datasets, leverages latent knowledge from pre-trained models, and focuses on creating compact models suitable for integration into any device and performance for specialized tasks. The toolkit offers features like prompt generation, image generation, dataset annotation, and tools for training small-scale neural networks for edge deployment. It provides hardware requirements, usage instructions, available models, and limitations to consider while using the library.
awesome-object-detection-datasets
This repository is a curated list of awesome public object detection and recognition datasets. It includes a wide range of datasets related to object detection and recognition tasks, such as general detection and recognition datasets, autonomous driving datasets, adverse weather datasets, person detection datasets, anti-UAV datasets, optical aerial imagery datasets, low-light image datasets, infrared image datasets, SAR image datasets, multispectral image datasets, 3D object detection datasets, vehicle-to-everything field datasets, super-resolution field datasets, and face detection and recognition datasets. The repository also provides information on tools for data annotation, data augmentation, and data management related to object detection tasks.
VisioFirm
VisioFirm is an open-source, AI-powered image annotation tool designed to accelerate labeling for computer vision tasks like classification, object detection, oriented bounding boxes (OBB), segmentation and video annotation. Built for speed and simplicity, it leverages state-of-the-art models for semi-automated pre-annotations, allowing you to focus on refining rather than starting from scratch. Whether you're preparing datasets for YOLO, SAM, or custom models, VisioFirm streamlines your workflow with an intuitive web interface and powerful backend. Perfect for researchers, data scientists, and ML engineers handling large image datasets—get high-quality annotations in minutes, not hours!
awesome-cuda-tensorrt-fpga
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depthai
This repository contains a demo application for DepthAI, a tool that can load different networks, create pipelines, record video, and more. It provides documentation for installation and usage, including running programs through Docker. Users can explore DepthAI features via command line arguments or a clickable QT interface. Supported models include various AI models for tasks like face detection, human pose estimation, and object detection. The tool collects anonymous usage statistics by default, which can be disabled. Users can report issues to the development team for support and troubleshooting.
awesome-LLM-resourses
A comprehensive repository of resources for Chinese large language models (LLMs), including data processing tools, fine-tuning frameworks, inference libraries, evaluation platforms, RAG engines, agent frameworks, books, courses, tutorials, and tips. The repository covers a wide range of tools and resources for working with LLMs, from data labeling and processing to model fine-tuning, inference, evaluation, and application development. It also includes resources for learning about LLMs through books, courses, and tutorials, as well as insights and strategies from building with LLMs.
awesome-LLM-resources
This repository is a curated list of resources for learning and working with Large Language Models (LLMs). It includes a collection of articles, tutorials, tools, datasets, and research papers related to LLMs such as GPT-3, BERT, and Transformer models. Whether you are a researcher, developer, or enthusiast interested in natural language processing and artificial intelligence, this repository provides valuable resources to help you understand, implement, and experiment with LLMs.
blog
这是一个程序员关于 ChatGPT 学习过程的记录，其中包括了 ChatGPT 的使用技巧、相关工具和资源的整理，以及一些个人见解和思考。 **使用技巧** * **充值 OpenAI API**：可以通过 https://beta.openai.com/account/api-keys 进行充值，支持信用卡和 PayPal。 * **使用专梯**：推荐使用稳定的专梯，可以有效提高 ChatGPT 的访问速度和稳定性。 * **使用魔法**：可以通过 https://my.x-air.app:666/#/register?aff=32853 访问 ChatGPT，无需魔法即可访问。 * **下载各种 apk**：可以通过 https://apkcombo.com 下载各种安卓应用的 apk 文件。 * **ChatGPT 官网**：ChatGPT 的官方网站是 https://ai.com。 * **Midjourney**：Midjourney 是一个生成式 AI 图像平台，可以通过 https://midjourney.com 访问。 * **文本转视频**：可以通过 https://www.d-id.com 将文本转换为视频。 * **国内大模型**：国内也有很多大模型，如阿里巴巴的通义千问、百度文心一言、讯飞星火、阿里巴巴通义听悟等。 * **查看 OpenAI 状态**：可以通过 https://status.openai.com/ 查看 OpenAI 的服务状态。 * **Canva 画图**：Canva 是一个在线平面设计平台，可以通过 https://www.canva.cn 进行画图。 **相关工具和资源** * **文字转语音**：可以通过 https://modelscope.cn/models?page=1&tasks=text-to-speech&type=audio 找到文字转语音的模型。 * **可好好玩玩的项目**： * https://github.com/sunner/ChatALL * https://github.com/labring/FastGPT * https://github.com/songquanpeng/one-api * **个人博客**： * https://baoyu.io/ * https://gorden-sun.notion.site/527689cd2b294e60912f040095e803c5?v=4f6cc12006c94f47aee4dc909511aeb5 * **srt 2 lrc 歌词**：可以通过 https://gotranscript.com/subtitle-converter 将 srt 格式的字幕转换为 lrc 格式的歌词。 * **5 种速率限制**：OpenAI API 有 5 种速率限制：RPM（每分钟请求数）、RPD（每天请求数）、TPM（每分钟 tokens 数量）、TPD（每天 tokens 数量）、IPM（每分钟图像数量）。 * **扣子平台**：coze.cn 是一个扣子平台，可以提供各种扣子。 * **通过云函数免费使用 GPT-3.5**：可以通过 https://juejin.cn/post/7353849549540589587 免费使用 GPT-3.5。 * **不蒜子 统计网页基数**：可以通过 https://busuanzi.ibruce.info/ 统计网页的基数。 * **视频总结和翻译网页**：可以通过 https://glarity.app/zh-CN 总结和翻译视频。 * **视频翻译和配音工具**：可以通过 https://github.com/jianchang512/pyvideotrans 翻译和配音视频。 * **文字生成音频**：可以通过 https://www.cnblogs.com/jijunjian/p/18118366 将文字生成音频。 * **memo ai**：memo.ac 是一个多模态 AI 平台，可以将视频链接、播客链接、本地音视频转换为文字，支持多语言转录后翻译，还可以将文字转换为新的音频。 * **视频总结工具**：可以通过 https://summarize.ing/ 总结视频。 * **可每天免费玩玩**：可以通过 https://www.perplexity.ai/ 每天免费玩玩。 * **Suno.ai**：Suno.ai 是一个 AI 语言模型，可以通过 https://bibigpt.co/ 访问。 * **CapCut**：CapCut 是一个视频编辑软件，可以通过 https://www.capcut.cn/ 下载。 * **Valla.ai**：Valla.ai 是一个多模态 AI 模型，可以通过 https://www.valla.ai/ 访问。 * **Viggle.ai**：Viggle.ai 是一个 AI 视频生成平台，可以通过 https://viggle.ai 访问。 * **使用免费的 GPU 部署文生图大模型**：可以通过 https://www.cnblogs.com/xuxiaona/p/18088404 部署文生图大模型。 * **语音转文字**：可以通过 https://speech.microsoft.com/portal 将语音转换为文字。 * **投资界的 ai**：可以通过 https://reportify.cc/ 了解投资界的 ai。 * **抓取小视频 app 的各种信息**：可以通过 https://github.com/NanmiCoder/MediaCrawler 抓取小视频 app 的各种信息。 * **马斯克 Grok1 开源**：马斯克的 Grok1 模型已经开源，可以通过 https://github.com/xai-org/grok-1 访问。 * **ChatALL**：ChatALL 是一个跨端支持的聊天机器人，可以通过 https://github.com/sunner/ChatALL 访问。 * **零一万物**：零一万物是一个 AI 平台，可以通过 https://www.01.ai/cn 访问。 * **智普**：智普是一个 AI 语言模型，可以通过 https://chatglm.cn/ 访问。 * **memo ai 下载**：可以通过 https://memo.ac/ 下载 memo ai。 * **ffmpeg 学习**：可以通过 https://www.ruanyifeng.com/blog/2020/01/ffmpeg.html 学习 ffmpeg。 * **自动生成文章小工具**：可以通过 https://www.cognition-labs.com/blog 生成文章。 * **简易商城**：可以通过 https://www.cnblogs.com/whuanle/p/18086537 搭建简易商城。 * **物联网**：可以通过 https://www.cnblogs.com/xuxiaona/p/18088404 学习物联网。 * **自定义表单、自定义列表、自定义上传和下载、自定义流程、自定义报表**：可以通过 https://www.cnblogs.com/whuanle/p/18086537 实现自定义表单、自定义列表、自定义上传和下载、自定义流程、自定义报表。 **个人见解和思考** * ChatGPT 是一个强大的工具，可以用来提高工作效率和创造力。 * ChatGPT 的使用门槛较低，即使是非技术人员也可以轻松上手。 * ChatGPT 的发展速度非常快，未来可能会对各个行业产生深远的影响。 * 我们应该理性看待 ChatGPT，既要看到它的优点，也要意识到它的局限性。 * 我们应该积极探索 ChatGPT 的应用场景，为社会创造价值。