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Manually/automatically re-run clustering on rejected faces

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评估

难度
4/5
预计耗时
3-5 天
新手友好度
35/100
Issue 类型
功能
描述清晰度
基本清楚
活跃度
停滞
技术栈
machine-learning, php

调研方向

先检查 clustering 是如何通过 occ 调用的,并将当前行为与 issue #951 进行比较。追踪手动分配的人脸如何成为新的 clustering 输入,然后将完成定义为:无需新照片即可对之前被拒绝的人脸重新运行 clustering。

由索引模型根据 Issue 内容生成。

描述

enhancement
Describe the feature you'd like to request

Last year, I added a few thousand photos to Memories over a few days. Recognize eventually churned through most of them and did a pretty good job of recognizing faces and tagging people. However, just a few weeks ago, I discovered the Unassigned faces page and there are so many photos and faces that remain unassigned. I spent a couple of hours assigning faces to ones that Recognize had already clustered together (most of which had a name assigned to them). I then assumed that Recognize would incrementally re-run clustering on rejected faces given the new information I provided by manually assigning a few. However, this does not seem to be the case.

Describe the solution you'd like

I would like a way to have Recognize re-run clustering on rejected faces given new stimuli (such as some rejected faces being manually assigned by the user). This would help me cut down on the number of unassigned faces I have.

I did come across https://github.com/nextcloud/recognize/issues/951 which says that clustering does grab a portion of rejected faces during the clustering of new faces. However, if I am not actively adding new photos, then this does not occur.

Describe alternatives you've considered

I've tried manually running clustering using occ but, as mentioned above, this will only work with new photos that have not yet been clustered. I'm hoping for a way to make the manual assignment of faces a new stimulus for attempting to cluster previously rejected faces.

主要语言
PHP
星标
698
派生
68
平均合并
18 小时 34 分钟
30 天内合并 PR
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环境准备

  • 没有 Dockerfile 或 Docker Compose 文件
  • 没有 Pull Request 模板
  • 阅读贡献指南

从这里开始

  1. 先读完整个 Issue,再读项目的贡献指南。
  2. 在 Issue 下留言说明你要接手 —— 这能避免两个人做同样的事。
  3. Fork 仓库,在一个分支上完成修改。
  4. 提交 Pull Request,并在描述里引用这个 Issue 编号。

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