XPUAI 发表于 2025-8-2 15:55:17

推荐!7个高质量AI开源项目 一起get起来!

心盘点了几个 GitHub 上或实用,或有趣的 AI 开源项目,一起来看看吧!EISeg 业界首个高性能交互式自动标注工具data:image/png;base64,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
EISeg 是由 PaddleSeg 团队联合 PPSIG Models-CV 小组韩霖、陈奕舟2位 SIG 成员发布的业界首个高性能的交互式分割自动标注工具。该工具可通过一次点击即可选取待标注目标,无需耗费大量精力依次选点,高效实用!除此之外,EISeg 支持多种图像及标注格式,轻松胜任目标检测、语义分割等标注任务,兼容大部分深度学习视觉套件。传送门:https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.2/contrib/EISeg
Paddle Fewshot Learning (PaddleFSL) 小样本学习工具包data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeUAAABGCAIAAAB9rRvBAAAUpklEQVR4Ae1d/0sb2dq//8z8MGyYLXJt3Vq6XrfUskXaUrsX0S0rFlbs1fSVku5Lv4joXsR7ee+mtKzFvQpvZKVaWWlESFgltEhbtrUtUcRGuHFAkzQao9Y05KfLmZM5mUwm58zETMyUB4KczDw555nP55zPeeY5Z8Y/JZIp+AACgAAgAAiUPwJ/Kn8XwUNAABAABACBRDIFeg23F4AAIAAIWAMB0Gtr8ATBBSAACAACoNeg14AAIAAIWAMB0Gtr8ASRBSAACAACoNeg14AAIAAIWAMB0Gtr8ASRBSAACAACoNeg14AAIAAIWAMB0Gtr8ASRBSAACAACoNeg14AAIAAIWAMB0Gtr8ASRBSAACAACoNeg14AAIAAIWAMB0Gtr8ASRBSAACAAChvU6tBl/txbyr4pvV4LwKUME/Kviu7VQaDNeWOcGfg+dU2Dw0CkoigMH5FFz/BrQ6/ju/kpwIyCG32/t7iU+alYHBw8dgb3Ex/dbuwExvBLciO/u6/cH+NWPlamWwKCp8Jas8oJ5pHioV6/ju/v+VTEU3abUBafKCoFQdNu/KuqUbOC3rLjDzgCDZUhKAS4Z4pFev169XglugFjToSzDs6Ho9kpwQ49jwK8elEpvAwyWHnMzWtTPI711XXod2owHxDC9IjhbnggExDAzlw38lid32CtgsJzZ0e+bHh6ZtenS63drofdbu8y6wKAMEXi/tftuLUR3DPgl+OwlkvGdD5uxeGRzOxyNFfyJbG5vxuLxnQ97iSSpvLACMGgUt2KRGI7GSswj80p16bV/VYQFRiaU5Wmwl/joXxXpvgG/iWRqL5Hciu8WLNCUH27Fdw+i2sAgvfcqz5pHYjgaKwGPymvRLOvS67crQc0fGzo4Njnd0+9sbO3scHT39DvHJqcN/RyMC0aASR/ToOCmrfLD+N4+RXCLciq+Z2Cvjgo3JkFMA1WFlK8L/mWvb55iULanSkBiOBozlUcmtqXQa3E9gpUai3Vjayf+dDi6xfUI00UwOCACzMHMNMjngLgeGZucvjfk0hzheIa+N+Qqc5bjOx+KosjMSuI7H/IhST/OJIhpQK+fnBXXI3hsljllxGFSKBmJSLJN45FcTr5CKfQai3VPv3PBv4z9IAre4ejO5xkcLxYCzMHMNMjnCZl6G1s77w25lGb3hlzkbDmzXJqgjEh5YdEZkyCmgZIaSpmwpmKT8pNyOFViEguOsg9Ok+l6PTY53dja2dPvzOUVdw5IjOQiU9wjzF7CNND0x+ubx8zioEwlylisF/zLmGUyVWtWdVgH9xJJoqQlKxSQy2YSxDTQgzDhscPR3djaaZUQ+1BIDEdjZvDIpMl0vaZwT/oH00swOAgCzMHMNNBsHes1TndglpVmOPdFbqQ0EyZK+0Mpm7TASJf+rbjhrVZMgpgGeuDFodXY5DQp6PnVodscCol4+dHotR+cJtP1OvdOWXmROFVibCb3ODhe4E7c9sYUr2oKDNfzAnd9Tlm5nnJgpNl2pHkkgKvaevbAXnNE4Hjhxmwq+5SiraSx8vZsX9WR2luze3r8McOG2UuYBppekVwnDqVVd9Dkzhqf1axB4yDmkUcU4E/9A4Nr3VL3sHvYHNHisvWlh93fnaqUfKi82D76OiPB60sP//dihU3g+D+f/H7w9//Ie/6WZhwNxz+T3K640P1wST6utSnQaGjGJIhpoAG1ohvjNUY8xWJLPAEv+JcX/MvGhqeiWqmqvYB7oOnr6jShR88N/MGmhu6t8iyNRAl58c2U4+yfz95dyjCIjs+0yx2M9DSO75qKxsJTXRwvtE/R6CNVFZ1H5aVpls3V6wX/Ml2vC7lZxnrNC1U9LzOXpFuvF361N3zRPJgW6NSzf9Zytkvpr8/7Knih6orzkXvY/Uf2KXUvpPU5cRZ10BuyZIiT7Ta+9sYnp9eJZEpcj+CBrRJrfAqHaR2ObgPJEInHmq5Rt9uDP75lg/Ocbr2mrVBNdXGVF9vvjj8cHWw/I3B8jWMOD+DVX5oEzlbzbf/4w6EfTtkE7sLga6zIUz+c6hh0TUy57nah49Xdv4fzjnmjC1ZMOWYaZEaK3JPvDbk6HN2YPrLSQJKTmDtyHO/p8vrmjWq3+Gsrxwu2evuAy+OeHO69eunWLBo7qjGS657OIzQS34y3XziO5ThHr1efTEw9JJ///+EUL3DfjwcM6nXReWRetVX1uupELcefGXgl66ZuvfZeFzheFmi576Zh0j3U6bAGHlzieEFPiEevp1hnmYOZaUDxhDLjMmdrjWolHg3H1EoedZO4GYuTQEldePP6xbqstm8Gz/JCxc2nyObpP07ywsn+F9g+MPY3jj+elnKFOj/5ex3HX/zpjVxDToi9GTP29kQmQUyDXKiVWnxvyJW7yWfBv4x3+ChlPbce6pHgYIPAfTXwbF8epzJTxRojNBKnuk9e7vtlou8sL+TodRY1T/rrOL6u95l00Eh8XXQeqWAiDK2q1/Zxj/2owJ3/eRF3BZVe7wTdd5orpMyG7Ytm+/jKNuoowcHzmRtt7vxwIJmS+g2Sb0nHyVmHV3EKgxj5Y9T+TbUN3UZVVnQ+FpOpxNpcb8u5dCun7YN/bCWSGvUkshUk8ny4Dd8e2qrrWpzecLorY0+cPs+tBnTzaDttH11On4rMOtN3lEeqG+77maSqDJiDmWmgqlD5FQdi94Zc+N5Z+Rcnu0jIpvxV3nI+vd5Hqaq6oxJBR881/WsukpYAidPzAyM/XqqwCUjolWi/ctbwAtcwnO4ksljg1vU+wSiOXyF6rRrMkpTnasGL/6tHeu3PEgXllBDZNPbeNCZBTINcwEkEzVxawPNuY2sn0zKnFUmvjzrc0Sy9zh1riZh/9HpzVZrfS7dm0i+9wYOi1zVQjwY7GrCqJtgk5uEoQ4c4014pB9eq+FqanrkLgy8Uk3Hmh9LTjyp/6F8LoElVoWX12pOK/GbneKHpV4naLL2WeomttuVfj93uxwNXamWzvUWfp7dR4Pgz9hGP24dEnOi1+Mrj7m/meKGh3+N2+0XFKQTZ8nC9TeC+bO11edzu0VvXR1HX8fTVXR92vwoGXo22nRC4o32+/ZT4yjPSdUZZj1JBtmdvV6Hbw74Rt8ftul1nE7gTfTj6kDyprvi6fWBSPnXauZBMJd4i0anqHPYtBhfG++ruGM7RM3sJ00DVaZRfSUqExGvKgmrTiPKH2mVtvd7z3qzl+Mq6OyhPMnLzHJfJhqXn4Pr+l2kFJ3odnbOfELgTDm+2WJB2lQOPUn59/yLHC1cmwshmrrtCGax50Ffu2ozy5+LL0SvVwrFrM+jmOv+HuKGnwCSIaaDZih7JPoBYI22NeBxVvMAdOXdr3C9PsVpjJDDccnlg5OmKuDjX2yBwfPPIGvq5NCgE7vzAMzmsUV0IBeH0KZZeLw01opskj0wWmZL/M9NeLXDVXVNkiUKLTZU/9K+F0aSs01y9xh2CMi2T6EzpE6NMBmRyY/SywNnsj6KphFKvn6I0dINLfi/dvn/gtMB9JWlfOv7N5EOIXqNGMzWTvoItFQ3lzPDY28X750iaBXeyTD4kU+3GSGNa1vGv8JTT9htK0eJftbnT6VopBmkdXU971TKOgnf0ybm1TB/P41gimWL2EqYBvQlxPeL1zeN7aizWuGwsssb+Yx4za0ES/mujDbxQcYc8dLf16KrA8fZHaMFZ0mubw0tgSaON5+xLg/I9Su4lsId6NBaY7DrGK5LU4aeOapK/7rtwUor3Zb2eupa+PzvWMf6apFO0Bnk4Gsv1h3KESRDTIF/lY5PTZGkx1+aAYo0rjDx3NuHA+ct2fA9KentmjCh77wzaUIBPqYeS0kwqs0lk6PWL3lMCd+ofTwhNab1e+umCwNku/vRS1nFikF3IBY1ypGCaSJ0m6jUhm7JGQbb0GViSysifJNM2oaLLE1HodZYES6RK2odSHHK+wqhez9l5gevyENTShZ2g98FA2zdnar7Ey9/patWdLOOwVI9yE4sinFS5nfkam7txAglBRYNjcDYoJXYk4c7pu2r3ZANmL2Ea5Ks597hyj0HuWfYRCRDFeuP8Ykw9j8qjHaON8yGKO2UJ7fqGSxxfa/fIk5wMhdIB1q10+MXdxs944bNvBjO57GgsvDTlwKtYlRdvjzr/qgi3l56iJSy03vi5wJ38gRKXlUM+hECBYybNJyRwRosSb5FKGIX9vUV3Xx3KT9beek6CIcUaz/7WwrjT3nKu5iucclTqdWa05rbCIjEWpuu16oZJzoecvXCR42vap0T6fFB0HnMvUHXERL3GZDOFGG/jNXDjnJE/RPzifTQyb/ziJPv5Mkonj9Li6LVSZ1HNOOg+d8Pl8S1uPPunnvi6IL1OphI7Gz7X7aYvkWpX3ZkzKtlMOWYaqDoN5WtR9Fq93pjNuB69bulyVPBC/YMViqu0papo7MVdlAY51jG+lCd3iUayNNo19n75+k7ywtn7qj1kmUit6OtUB2EQx1WaN0N4MbkIeo1HYnSiRY57VDHNs55ajkcJTO+roDiJ8pyK+Jqm13QSEUdUvf795nH1yrAUX3/b0SUlvhT7OLPDaqzjReeR0l3xKbP0mjz8xvQgkUwZW5hSjV6c7jh9Bq0sYUmVDNT5EJwLLjAfIgVxRx1ZO77XJ5pIi8mU7061jnwIrgeluTEs6XyIlANRTTOZr7JxIrk1ekXg+PSNgh5gsQ1zMDMNdLZFpl7Nwa+rEsUNR8ZeOqjOh9jsbpIPUa5EpbvHlvc6kgBKiE3bCjbXfYxn5qBF1/cCV9k9kyvoLL0u+j6wgzCI42tCmdc3T/btYUKLptf7HnSfevWxvG5E4uuXtxTLiaILLSPp1GsaiVhhaXr9ovcvAvcXRTJEjq/bp8SpazXMELvoPGb6vBxrqo6YpddYgnEek/mXGKuc0/6q0utkKvG8D61pEPXcX0G7iLLWGzPjFk/s9T8+fvTrnHJ/CGoru+aMYpJlk68dg5PyeuP+nB2tFtoHZ+e8D+xV6AEKORCQ6qnqHHY/8DzLrhYvv2StN8q7F5TNZYWQnr66O6O+xY3Fp8NoVVOpTXlIVeHGHMxMA1WFml9J+gunsJn3VZqV4HUIdXydxOKbtd4ox87a+RA02tPdIG8Km/KoxZRD4Pi69kHFFt2Z1yIa/0s/X/tb79DUw4l/327KGs9Tf//uf+6Ooy29Q90oH2L77pf8j8wU/TmLgzBIdmR6ffNkO3ZPvxMLN/35CW0SM90yONLS3HbTKa2uO9vqKzleaPtNSlJljRGJRNulXvecd3KgQdrWpVOvKSSmUxlKvV4Z//Zz4ZhjRqIyFvajnZqcI2u5OPO8TPg1M4VddB5ZeJq2nw/PzMqtAsyy3hGerarSFeL9A3J8nUwloi9HrqMNXijt+3XrwKwijxmd78Ub5i6jPR5ZKpldc9apZEqcGWg6jTocd6S6oWdOTKYiT6VtRnxlzdUJN0rLyHq9vzJ69Qza+Xe0z5et1+hJAWU91ycWd9Kxtqq5zNe3Pzd8IbWbvf+PSS0xYA5mpgGpilIg6S8s3Jr5UMrP06c042u0yrrh/rEVP3qq2KAprzcq5zAliQFpV0/+LSJ5HmWW1poya55SKFCPn4tZfdhVX/G51K/OfNc7nclvPhmUn4e0HT91uY/yfGO5PY+OicN/sTqTMjnFJi6j0en+LP1kw9vTXIN7L19ZUd/aK2/US6jGyPJEmzS4KhoGfOOq9UZ5WGk3kcpDopx9ouj1JHqU8a/Dq2llx/E42R+Ccyno0SftLSJm8MjE2az4Gj/hptyNSy8zHQWDghFgyjHTQE/TeGyTt0/kPvGop5IS27CjM62sZdbwNm5gNCgze4cPCaSUb9Bc8C8T1W5s7SwxL4aaOxQSP9n3PRHo8eNSeDyPTU5TNo2Qn0ChKAgw5ZhpoMeN4uRD8sRQehwozKbEr+Isw/epdji68eNOuQAu+Jfxk+i5p8rqSIlJ/JTfp0p4Vb0eCPSaIGN2gSnHTAOdHuK3p45NTutNbZVcnTUvhL1mZTyI1ozBjS5PEW+ZBDENSFWfaqFkJH76/6+AdBGvb76n39nh6C7ainN5DHhygeVZYA5mpkF5XlcRvSpBgFZYZI2vkUkQ06CIWJVtVSUgseDIWiePTGxNzF8z2waD0iDAHMxMg9L4ebitmPevWg/4f1rNzl8fLuzFbd08Eg/+/3b18MhEQ5dew//PZuJYtgbw37UNUbOXSMZ3PmzG4uwH56hJksjm9mYsHt/5UMDqosphYFAFCPNrsUgMS290KiWPzEvTpdfv1kLvtwz/Uwxm22BQAgTeb+2+WwvRGwJ+6fgc7llg8HDxL1brenhktqVLr0Ob8YAYZtYFBmWIQEAMhzYZb1sGfsuQOOISMEigsHRBD4/MC9Sl14lkaiW4EYoae2kvs20wMBuBUHR7JSi/p5C6Ngv8ms1FYfUDg4XhVm6/0s8j3XO9eh3f3feviiDZdDTL6mwouu1fFeO7+3q8An71oFRiG2CwxICb1JwhHuk+6NXrRDIV391fCW4ExPD7rd29xEd6vXD2sBDYS3x8v7UbEMMrwQ2dYo1dBX4PizJVu8CgChCLfi2YR8r1GtBrXEtoM/5uLeRfFd+uBOFThgj4V8V3ayFmzjpfnwB+D51TYPDQKSiKAwfkUXOEGtZrzVrgICAACAACgIDZCIBeK18nBmVAABAABMoXAdDr8uXG7Lka6gcEAAFrIQB6DXoNCAACgIA1EAC9tgZP1ooCwFtAABAwAwHQa9BrQAAQAASsgQDotTV4MmOuhjoBAUDAWgiAXoNeAwKAACBgDQRAr63Bk7WiAPAWEAAEzEAA9Br0GhAABAABayAAem0NnsyYq6FOQAAQsBYCoNeg14AAIAAIWAMB0Gtr8GStKAC8BQQAATMQAL0GvQYEAAFAwBoIgF5bgycz5mqoExAABKyFAOg16DUgAAgAAtZAAPTaGjxZKwoAbwEBQMAMBECvQa8BAUAAELAGAqDX1uDJjLka6gQEAAFrIQB6DXoNCAACgIA1EAC9tgZP1ooCwFtAABAwA4H/AhTsKrulnBtfAAAAAElFTkSuQmCC
PaddleFSL 是基于飞桨的小样本学习工具包,旨在降低小样本学习研究和应用的设计与时间成本,内置多种经典小样本策略以及低层次的工具函数与接口,使用者可轻松完成小样本训练任务,甚至是新的小样本策略。除此之外,在代码仓库中作者还提供了大量小样本论文、数据集等学习资源,小样本入门有这个项目就够了!传送门:https://github.com/tata1661/FSL-Mate/tree/master/PaddleFSL
PyCorrect 中文文本纠错工具data:image/png;base64,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汉语的表达能力丰富且多样,相较于英文纠错,“词”粒度和语境判断显然不足以满足汉语的纠错需求。Pycorrector 就是这样一款优秀的深度学习中文文本纠错工具,其可对音、形、缩写、语境、顺序等8种常见的错误形式进行检测和纠正,也可应用于简单的稿件检测和论文错字检测。传送门:https://github.com/shibing624/pycorrector
AgentOCR 支持核显加速的 OCR 工具data:image/png;base64,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AgentOCR 是一款基于 PaddleOCR 但更加适合桌面部署的增强版 OCR 工具箱,隶属于 AgentMaker 开发小组。在推理方面其不仅继承了 PaddleOCR 超轻量级模型的速度优势,还率先支持了 Windows DirectX 推理加速(DML),使得推理支持核显且不挑显卡,释放硬件性能!除此之外 AgentOCR 还配备了图形化半监督自动标注工具、普通车牌识别、一键服务化指令等常见 OCR 能力,增强用户体验!传送门:https://github.com/AgentMaker/AgentOCR
QGUI 轻量级、低门槛 GUI 框架data:image/png;base64,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 是一款基于 Tkinter 的 0.1MB 轻量级 Python 图形化程序生成框架。在使用难度方面,开发者无需过多担心图形化开发的学习成本,几行代码即可为个人深度学习程序提供图形化界面。此外,QGUI 还内置了不限于飞桨、LayUI 等多种主题风格,无需过多配置,一行代码轻松完成!传送门:https://github.com/QPT-Family/QGUI
Face.evoLVe 高性能人脸识别库data:image/png;base64,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
目前人脸识别开源项目众多,但真正能方便快捷拿来就用、性能指标业界领先的开源库并不多,而最近刚刚开源的 face.evoLVe 绝对是不容忽视的力量。作者不仅开源了代码(包括人脸识别的训练和测试的各个环节)和高精度预训练模型,连业界主流的数据集也都提供给大家下载,方便学术界和工业界使用和进一步研究,强烈推荐大家关注!传送门:https://github.com/ZhaoJ9014/face.evoLVe

全新 OpenCV 5.0data:image/png;base64,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
OpenCV 是一个跨平台的开源的计算机视觉库,提供了 Python、Ruby、MATLAB 等语言的接口。此次更新的 OpenCV5.0 不仅仅支持常规的图像分类、目标检测、图像分割和风格迁移功能,还引入上百个的 CV 方向任务。同时还支持更多深度学习框架,例如在 OpenCV DNN 模块中增加 PaddlePaddle 飞桨模型的支持。飞桨一系列特色模型可快速通过 OpenCV 进行加载和推理,极大方便了应用开发者,大大加速开发流程。传送门:https://github.com/opencv/opencv
页: [1]
查看完整版本: 推荐!7个高质量AI开源项目 一起get起来!