Comments (3)
你可以修改 mmdet/utils/active_datasets.py
文件中的 update_X_L
函数(它在 tools/train.py
中被调用),令其返回 X_S
,此即为每一轮主动学习后筛选出不确定性较高的新增样本。
You can modify the update_X_L
function in mmdet/utils/active_datasets.py
called by tools/train.py
, make it return X_S
, which are the new selected samples with higher uncertainty after each cycle of active learning.
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你可以修改
mmdet/utils/active_datasets.py
文件中的update_X_L
函数(它在tools/train.py
中被调用),令其返回X_S
,此即为每一轮主动学习后筛选出不确定性较高的新增样本。You can modify the
update_X_L
function inmmdet/utils/active_datasets.py
called bytools/train.py
, make it returnX_S
, which are the new selected samples with higher uncertainty after each cycle of active learning.
感谢,我现在发现mmdet2.3.0已经有了支持windows的版本,那是不是在win上运行就有可能了
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是的,但我没有尝试过,你可以试一下。
Yes, but I have not tried it. You may have a try.
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Related Issues (20)
- 关于修改配置应用到自建数据集的问题
- Why do we need Annotation files for unlabelled dataset?
- Training each cycle from scratch?
- A request
- No detection of testing of custom train model HOT 8
- Using mmdetection to test checkpoint file get 0 in mAP HOT 1
- comparison with other baseline methods(random, entropy) HOT 4
- Unable to solve error of version. HOT 3
- 关于初始化X_U数据集使用 HOT 2
- BDD100K下进行测试 HOT 2
- Welcome update to OpenMMLab 2.0 HOT 1
- 出现以下问题,不知道如何解决 HOT 1
- comparative experiment HOT 2
- document-layout-analysis HOT 1
- saving generated annotations in each active learning cycle HOT 4
- 成功运行实验,但过程除了一些问题。
- Why does it split to 2007 and 2012 folders? HOT 1
- 安装与编译mmdetection时报错,请问这是什么原因呢? HOT 3
- code to generate figures 6 & 7
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