Comments (2)
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?
Hello, you can detect "unseen unknowns" objects. The ability to detect unknown objects mainly depends on the training set (the more categories of training set, the stronger the ability to predict unknown classes) and the parameter unknownconf (the greater the threshold, the less unknown objects will be predicted).
I have uploaded the code required for the complete detection process, but the training code will not be uploaded temporarily. If you need models trained under other public data sets, you can leave a message in the comments area.
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The actual detection effect can be viewed by clicking the demo link on the home page
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Related Issues (18)
- 1 楼
- 提个问题,大佬有做增量学习这块吗? HOT 1
- 请问在逻辑推理阶段是使用了能量模型吗? HOT 1
- 论文发布了吗
- unknownmode是什么意思 HOT 3
- 大佬啥时候支持训练啊
- loss文件什么时候能更新?
- 训练模型识别未知的负样本
- 您能上传loss.py文件吗?
- 期待大佬更新 HOT 1
- IndexError: list index out of range HOT 1
- detect.py? HOT 1
- 没有发现哪里有做开集识别的地方。。。 HOT 2
- 期待!什么时候可以发布完整的代码或者说明
- 缺少loss文件 HOT 2
- Will there be any related papers to be released?
- 请问我今生还能等到release吗?
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