Comments (5)
yu4u
commented on June 27, 2024
2
@bsonnenschein is right. And I saw "batch_input_shape": [null, 32, 32, 3] in WRN_16_8.json =D
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yu4u
commented on June 27, 2024
1
Hi, you seem to have trained the network using images with 32x32 size.
Therefore, img_size = 32 will fix the issue.
from age-gender-estimation.
bsonnenschein
commented on June 27, 2024
1
It is evident because 131072/32768 = 4 just as 64x64/(32x32) = 4.
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taewookim
commented on June 27, 2024
Oh wow. Once again, thank you SO much for your help. That worked!
Out of curiosity.. how were u able to figure that out w/o looking at my code?
from age-gender-estimation.
taewookim
commented on June 27, 2024
wow.. you are sharp indeed. never even occurred to me to calculate that.
once again, thank you @bsonnenschein and @yu4u
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Related Issues (20)
- onnx HOT 1
- Citation for this repository? HOT 2
- Low gender accurate HOT 1
- train doesn't use detection's result HOT 1
- Requirement versions
- How to draw ROC curve？
- cannot import name 'EfficientNetB0' HOT 3
- Hi! The face image in folder should be cropped? HOT 1
- accuracy in "age_estimation" folder
- run demo.py in "age_estimation" folder HOT 2
- Turn off logging outputs
- Variable shape and weights shape are not matching HOT 5
- Please help me to custom model only age-estimation
- How to re-traning when stop HOT 1
- cv2.imshow returns error
- labels.txt for age and gender
- why dot ages arange(0, 101) rather than max in predict HOT 1
- You can consider using the new B3FD dataset or IMDB-WIKI filtration lists for even better results HOT 1
- OmegaConf error
- mae performance results are different in Debug mode and Release mode.
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