Comments (6)
I.e. Check https://arxiv.org/abs/1806.02658
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Yes, it could be. I think that's kind of controversial because that post focuses more on RGB images, I mean as a results, where those artifacts matter more than in semantic segmentation.
I see different architectures with upsamplings and with deconvolutions but I do not see any community agreement.
I edited the code now with the upsampling layer (I did not deleted the traspose convolution code). Nevertheless, Deeplabv3 architecture relies on the upsampling in stead of the convolutions, so I guess as MNasnet is also from Google, I'll go with the upsampling haha
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What do you think?
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Yes Deeplabv3 Deeplabv3+ and follow-up https://arxiv.org/abs/1809.04184 use up sampling.
In MNasnet paper segmentation was in "future" section so there is any reference implementation. Please check on the last work I mentioned if you find something interesting.
I've also mentioned your work in keras-team/autokeras#81
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Sure!, thanks a lot! :D
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If you are interested the mentioned arxiv paper code is available at tensorflow/models#5430
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Related Issues (12)
- Why are all data sets labeled black and black?
- Why are all the predicted data sets black? HOT 4
- about saving model
- Results are strange HOT 3
- Error with different dataset HOT 8
- Bilinear Upsample HOT 3
- Multiprocess load batch HOT 11
- Move to Dataset API HOT 2
- where is imgaug HOT 2
- is there a plan to add detection function? HOT 1
- Can you tell me the corresponding network structure. HOT 1
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