Some thoughts on the paper. about self-correction-human-parsing HOT 7 CLOSED

1. The number of epochs is chosen as 150 to make a fair comparison with CE2P.

2. Usually, it will.

3. Due to computational limitations, currently, I have no experience with a much more large dataset. However, LIP is already a large dataset with 30000+ training samples.

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@PeikeLi I have found the detectron2 in your codebase. I suppose it is for multi-human parsing. I wanted to check if there is any document/tutorials on how to use it with SCHP ?

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@VyBui Any challenges you faced while training it on a custom dataset? I have my own images and annotated segments, do I need to change anything? I understand I should have them ordered according to the structure of LIP dataset, but are there any other caveats?

Would appreciate a response. Thanks!

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@rkhilnani9
I have written a few necessary steps to train on a custom dataset here: #14.

There are challenges I have faced while training SCHP with a custom dataset:

1. Pytorch version has to be 1.2. I have never tried with 1.3, 1.4 but definitely not Pytorch v1.5
2. Remember to set num_classes to the number of classes in your segmentation image.
3. There is a bug on training code but I have seen the author fix it.
4. set GPUs to the number of your GPUs local machine.
5. Make sure you follow the LIP dataset convention and then you will be just fine.

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closed

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hello, have you found how to deal with the multi human parsing task via SCHP?

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hello, have you found how to deal with the multi human parsing task via SCHP?

well, I think it is a step to step (from single instance segmentation to human parsing, then convert and identify them) process, right?

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