training with new dataset #15
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Hi, fg means 'foreground images' and bg is 'background'. Your input images are foreground images and you need one more folder for background images. You can refer to our paper or Deep Image Matting for more details about the composition. |
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Thank you very much Li
so maybe you are doing the same procedure than they do for example at
HAttmatting? like this?:
- you get a series of foreground images + their ground truth alpha masks
- you compute the pure foreground colors to produce the final foreground
image
- you then do compositing of the foreground images with random COCO
backgrounds using the alpha mask ground truth for the compositing
- finally you obtain the training input dataset, made by the composited
images + ground truth masks
but in your model are you doing the compositing before the training or
during the training?
maybe thats why you ask as input the foreground and the background images?
…On Wed, Oct 14, 2020 at 3:07 PM Li Yaoyi ***@***.***> wrote:
Hi, fg means 'foreground images' and bg is 'background'. Your input images
are foreground images and you need one more folder for background images.
You can refer to our paper or *Deep Image Matting* for more details about
the composition.
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Yes, almost the same as you said, and we composite images during the training. The number of samples in each epoch is the same as background images. |
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thank you Li, that´s interesting, in HAttmatting they first do the
compositing ahead of training, but you do it during the training,
interesting,
in theory that should slow down the training right?
"and we composite images during the training "
…On Mon, Oct 19, 2020 at 3:27 PM Li Yaoyi ***@***.***> wrote:
Yes, almost the same as you said, and we composite images during the
training. The number of samples in each epoch is the same as background
images.
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It highly depends on your CPUs and the complexity of your augmentations. In our training, we are doing the compositing after the resize operation, which is faster than merging high-resolution images off-line. It is a little slower but totally acceptable. |
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thank you Li, makes sense
…On Mon, Oct 19, 2020 at 3:43 PM Li Yaoyi ***@***.***> wrote:
It highly depends on your CPUs and the complexity of your augmentations.
In our training, we are doing the compositing after the resize operation,
which is faster than merging high-resolution images off-line. It is a
little slower but totally acceptable.
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quick question if I may, when designing a dataset to extract people like
the one you used, would you also include people holding objects? (books,
hats, fruit, etc) or better people not holding anything?
thank you for any advice, best :)
…On Mon, Oct 19, 2020 at 3:46 PM Javier Ideami ***@***.***> wrote:
thank you Li, makes sense
On Mon, Oct 19, 2020 at 3:43 PM Li Yaoyi ***@***.***> wrote:
> It highly depends on your CPUs and the complexity of your augmentations.
> In our training, we are doing the compositing after the resize operation,
> which is faster than merging high-resolution images off-line. It is a
> little slower but totally acceptable.
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> <#15 (comment)>,
> or unsubscribe
> <https://github.com/notifications/unsubscribe-auth/AAHUIBBVMT4DF3AFUH5L3V3SLQ7BPANCNFSM4SB5QUYQ>
> .
>
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and also if I may, while creating the dataset I have the doubt if it would
be important to have people from different races, caucasian, asian, black,
etc; I don´t know if this is relevant when we are just extracting shapes;
is it relevant for this type of dataset or could the dataset be using just
caucasian people for example; I have doubts because for example networks
that extract saliency maybe wouldn´t care about it, or maybe they do care,
any tips are welcome, thank you :)
…On Mon, Oct 19, 2020 at 5:40 PM Javier Ideami ***@***.***> wrote:
quick question if I may, when designing a dataset to extract people like
the one you used, would you also include people holding objects? (books,
hats, fruit, etc) or better people not holding anything?
thank you for any advice, best :)
On Mon, Oct 19, 2020 at 3:46 PM Javier Ideami ***@***.***> wrote:
> thank you Li, makes sense
>
>
>
>
>
>
>
>
> On Mon, Oct 19, 2020 at 3:43 PM Li Yaoyi ***@***.***>
> wrote:
>
>> It highly depends on your CPUs and the complexity of your augmentations.
>> In our training, we are doing the compositing after the resize operation,
>> which is faster than merging high-resolution images off-line. It is a
>> little slower but totally acceptable.
>>
>> —
>> You are receiving this because you authored the thread.
>> Reply to this email directly, view it on GitHub
>> <#15 (comment)>,
>> or unsubscribe
>> <https://github.com/notifications/unsubscribe-auth/AAHUIBBVMT4DF3AFUH5L3V3SLQ7BPANCNFSM4SB5QUYQ>
>> .
>>
>
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Dear friends, couple of quick questions:
in your gca repo, what is the difference between the
gca-dist.toml and the gca-dist-all-data.toml files?
and then in the config file, here:
train_fg = "/home/liyaoyi/dataset/Adobe/all/fg"
train_alpha = "/home/liyaoyi/dataset/Adobe/all/alpha"
train_bg = "/home/liyaoyi/dataset/coco_bg"
test_merged = "/home/liyaoyi/dataset/Adobe/Combined_Dataset/Test_set/merged"
test_alpha =
"/home/liyaoyi/dataset/Adobe/Combined_Dataset/Test_set/alpha_copy"
test_trimap =
"/home/liyaoyi/dataset/Adobe/Combined_Dataset/Test_set/trimaps"
If i want to retrain the net with the distinctions-646 dataset,
it's clear what I should put in the first 3, just foregrounds, just alpha
and just coco backgrounds,
but in regards to the 3 test folders, the distinctions dataset does not
have trimaps in separate files, would it be possible to use part of the
alphamatting dataset at http://alphamatting.com/datasets.php as the test
folders here? (they have separate trimaps
thank you for any tips
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Good day friends,
I am running well the training code of GCA, however using a single 16GB RAM
gpu of colab pro, just to try it, I get Memory error:
RuntimeError: CUDA out of memory. Tried to allocate 3.29 GiB (GPU 0; 15.75
GiB total capacity; 11.80 GiB already allocated; 2.16 GiB free; 12.43 GiB
reserved in total by PyTorch)
[ ]
How much GPU memory is required to train GCA?
thank you
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good day, changing the images from png to jpeg format eliminates the memory
problem thats good,
and the training part goes well, however the test part fails with error:
" ValueError: operands could not be broadcast together with remapped shapes
[original->remapped]: (3,2) and requested shape (0,2)"
for the training part im using the distinction-646 dataset, for the test
part I am using the alphamatting dataset because it has trimaps,
could that be the reason? what should be shapes and features of the images
used for the test part?
thank you :)
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…On Wed, Oct 28, 2020 at 5:41 PM Javier Ideami ***@***.***> wrote:
Good day friends,
I am running well the training code of GCA, however using a single 16GB
RAM gpu of colab pro, just to try it, I get Memory error:
RuntimeError: CUDA out of memory. Tried to allocate 3.29 GiB (GPU 0; 15.75
GiB total capacity; 11.80 GiB already allocated; 2.16 GiB free; 12.43 GiB
reserved in total by PyTorch)
[ ]
How much GPU memory is required to train GCA?
thank you
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Good day
I want to try to train the model with a new dataset I´m creating,
In this new dataset, for each input image, I have a ground truth alpha mask.
In here:
train_image_file = ImageFileTrain(alpha_dir=CONFIG.data.train_alpha,
fg_dir=CONFIG.data.train_fg,
bg_dir=CONFIG.data.train_bg)
It seems to ask for 3 folders, one for the ground truth alpha it seems, and then two for fg and bg?
what are these?
if I have input images and ground truth alphas, how can I adapt them to work with the input of your model?
thank you very much
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