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Detect known and unknown objects in the open world(具有区分已知与未知能力的全新检测器))

Python 98.19% Shell 1.21% Dockerfile 0.61%
yolov5 pytorch yolo object-detection deep-learning unknown autolabel open-world-detection open-world yolov8

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ow-yolo's Issues

Would this be able to detect true "unseen unknowns"?

I've seen many open-set object detection projects that can detect "seen unknowns" (classes that are already present, although unlabeled, in the training pipeline).

But I'm looking to detect "unseen unknowns" (classes that do not exist in the image space of the dataset at all). Is this project capable of doing this, at least to certain capability?

1 楼

这里什么也没有

损失函数

兄弟,论文发表了吗,可以上传一下损失函数吗,或者私发我也行[email protected],谢谢(我不做通用领域的,不用目标检测开放数据集,和你的论文没有冲突)

训练模型识别未知的负样本

你好我想问一下,添加未知类分数训练模型区分已知未知时,是否有负样本的参与,即手动标注的未知对象。如果没有又是如何训练模型在已知类上的未知分数趋近于0。

IndexError: list index out of range

使用您提供的预训练权重预测会出现

IndexError: list index out of range

请问:

  1. 所提供的权重是基于coco还是voc训练的?
  2. 是否可以提供下与权重相搭配的yaml配置文件?

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