Comments (3)
Hi, @kkjh0723
Did you have to make any changes to the code in order to get it working? I am also trying to replicate the CLIP result but unable to do so.
Thanks!
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@ayushchakravarthy ,
If I remember correctly, there were some minor changes required to run CLIP.
In the following lines,
I changed result_tmp[i][0][1]
to result_tmp[i][0][0]
and result_tmp[i][1][1]
to result_tmp[i][1][0]
.
Also, in this lines, I changed it as following,
sample_t = random.sample(sample_true,self.sample_num if len(sample_true)>self.sample_num else len(sample_true))
sample_f = random.sample(sample_false,self.sample_num if len(sample_false)>self.sample_num else len(sample_false))
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Hi,@kkjh0723
Have you reproduced the results of this work? I have tried many times, but the end result is not satisfactory.
I used the CLIP(ViT-B/32) as my model. And I select the "ITM" task to test.
For the final average scores,
Attribute : 68.6477405706409
Relation : 74.7221415628598
Object : 89.4515112110188
The result is much higher than the paper. So I'd like to know how much data you used, since your results don't vary that much.
Thank you!
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Related Issues (10)
- Object features for Oscar model
- Running test with other models HOT 1
- PIC dataset is not available anymore HOT 3
- Difference between code and description in the paper HOT 1
- Annotation path in Relation corpus is wrong HOT 1
- Question about the visualization results shown in Figure5 and Figure6
- Why attention demo chooses language model layer to catch model attention?
- demo链接失效了
- How to generate the negative caption? HOT 1
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