Comments (2)
Hello, the easiest way to implement our code on your dataset is to modify the file directory of your dataset to be the same as MVTec. Then, you should change the "train_path" and "test_path" to your own dataset.
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您好,在您的数据集上实现我们的代码的最简单方法是将数据集的文件目录修改为与 MVTec 相同。然后,您应该将“train_path”和“test_path”更改为您自己的数据集。
这个虽然漏检率很低,但是常常把正常样本也检测出异常,有没有好的方法能避免,大佬
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Related Issues (20)
- The relationship ? HOT 15
- AssertionError: set(masks.flatten()) must be {0, 1} HOT 7
- train result HOT 1
- Code for cifar10 HOT 1
- What does the function "def vis_nd(name,_class_) "in test.py do HOT 2
- The problem about the evaluation HOT 1
- Change the Encoder-Decoder Architecture
- How can i infer a single image?
- Some minor corrections in the test.py script
- loss funtion problem HOT 2
- memory complexity and reasoning time
- How to calculate the predicted mask ?
- How to use "def vis_nd(name,class) "
- custom data with different input size
- Why the PRO should be normalized from 0 ~ 1 to 0 ~ 0.3?
- 关于正常样本常常误判 HOT 5
- 可以询问一下这个问题怎么解决吗
- 我想询问一下,数据集训练的epoch设置为200的原因。
- 为什么编码器设置为评估模式而不是设置为参数冻结
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