Comments (2)
@GuohongLi Thank you very much for asking and sorry for the late reply. As replied in your last issue (#50 (comment)), we finally debugged the published code and current open set performance is:
============
Phase: test
Evaluation_accuracy_micro_top1: 0.361
Averaged F-measure: 0.501
Many_shot_accuracy_top1: 0.442 Median_shot_accuracy_top1: 0.352 Low_shot_accuracy_top1: 0.175
==========
This is higher than we reported in the paper. We updated some of the modules with clone() method, and set use_fc in the first stage to False. These changes will lead us to the proper results. Please have a try. Thank you very much again.
For Places, the current config won't work either. The reason why we could not get the reported results is that we forget that on the first stage, we actually did not freeze the weights. We only freeze the weights on the second stage. We will update the corresponding code as soon as possible.
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@GuohongLi Hello, we just updated new configuration files for places, and the newly implemented results are a little better than reported. Please check out the updates. Thanks!
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Related Issues (20)
- Reproducing OLTR results HOT 3
- Stage 2 multi GPU
- why fix all parameters except self attention parameters? HOT 4
- Table 2 results HOT 2
- Pretrained Weights for Places_LT?
- the use of fc layer HOT 2
- the accuracy of the train and val HOT 2
- how to compute centroids?
- Why the input dimension of the `fc_spatial` layer in `ModulatedAttLayer` is 7*7*in_channel? HOT 1
- Many_shot_accuracy_top1: nan on my own dataset HOT 1
- Revised F-measure results for other models in your paper
- Applications for face recognition
- Error when running stage_1.py under Places_LT
- Unable to reproduce baseline result on ImageNet-LT HOT 1
- BUG: stage1 test error!!
- Could you please give me an example of arranging ILSVRC2014 dataset? HOT 7
- Implementation on Inat-18
- About Class aware sampler
- The role of untrained FC(add_fc)
- The question about the version of Places_LT
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