How to train my own dlib_face_recognition_resnet_model_v1.dat #11
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@davisking I'm also interested if you're willing to release the dataset. In any case, I understand that it involved a lot of work. |
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You can use the trillion faces or ms celeb datasets. They are already available and very large. |
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Davis, is there a python API for training your own model? Thank you, sir! Dlib is amazing. |
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No |
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@davisking , I don't know how to train my dlib_face_recognition_resnet_model_v1 model, can you give me some methods! |
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This example program goes into the details: http://dlib.net/dnn_metric_learning_on_images_ex.cpp.html |
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@davisking Just to be sure, is that C++ code the one used to train this model? I'd like to transfer learn on my own dataset and want to be sure I'm starting from the right place |
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Also we’re rhe input images aligned centered cropped etc or just resized to 150x150 with a crop from a face finding model? Also be helpful to know this just in terms of using the base model - I see the sample code that uses it does this so I presume it was trained that way As well |
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The notes on how this model was trained are in the example program that shows how to use it: http://dlib.net/dnn_face_recognition_ex.cpp.html |
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Ah thanks for that! Very helpful.
I managed to get it to train a few steps on some images of my own. However
I couldn’t load it into a net_type because the saved weights have the
affine layers while the net_type has batchnorm layers. Though I see it auto
converts from batchnorm to affine when saving, I wasn’t able to get it to
auto convert from affine to batchnorm to keep training. So I have been
training the anet_type directly.
Is there a way to retrieve the net_type from the saved weights on the repo?
Or are part of the batchnorm parameters lost when saving the model meaning
I can’t transfer learn it?
…On Thu, Oct 31, 2019 at 12:02 AM Davis E. King ***@***.***> wrote:
The notes on how this model was trained are in the example program that
shows how to use it: http://dlib.net/dnn_face_recognition_ex.cpp.html
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Some things are lost when converting from batch norm to affine. Save in
batch norm form when doing training.
…On Thu, Oct 31, 2019 at 9:17 AM Claudiu ***@***.***> wrote:
Ah thanks for that! Very helpful.
I managed to get it to train a few steps on some images of my own. However
I couldn’t load it into a net_type because the saved weights have the
affine layers while the net_type has batchnorm layers. Though I see it auto
converts from batchnorm to affine when saving, I wasn’t able to get it to
auto convert from affine to batchnorm to keep training. So I have been
training the anet_type directly.
Is there a way to retrieve the net_type from the saved weights on the repo?
Or are part of the batchnorm parameters lost when saving the model meaning
I can’t transfer learn it?
On Thu, Oct 31, 2019 at 12:02 AM Davis E. King ***@***.***>
wrote:
> The notes on how this model was trained are in the example program that
> shows how to use it: http://dlib.net/dnn_face_recognition_ex.cpp.html
>
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Is there any way to get the batch norm form of the model on the repo? Or is
this reserved for secret sauce not for public consumption? :). I understand
if it’s the latter.
If it’s not available I suppose we can’t transfer learn it as-is. If we
load weights and swap out the affine for a random batch norm it seems
that’d lose all the learning in first few steps. Best option seems to be to
keep the affine as-is. Let me know what you think.
On Thu, Oct 31, 2019 at 11:52 PM Davis E. King <[email protected]>
wrote:
… Some things are lost when converting from batch norm to affine. Save in
batch norm form when doing training.
On Thu, Oct 31, 2019 at 9:17 AM Claudiu ***@***.***> wrote:
> Ah thanks for that! Very helpful.
>
> I managed to get it to train a few steps on some images of my own.
However
> I couldn’t load it into a net_type because the saved weights have the
> affine layers while the net_type has batchnorm layers. Though I see it
auto
> converts from batchnorm to affine when saving, I wasn’t able to get it to
> auto convert from affine to batchnorm to keep training. So I have been
> training the anet_type directly.
>
> Is there a way to retrieve the net_type from the saved weights on the
repo?
> Or are part of the batchnorm parameters lost when saving the model
meaning
> I can’t transfer learn it?
>
> On Thu, Oct 31, 2019 at 12:02 AM Davis E. King ***@***.***
>
> wrote:
>
> > The notes on how this model was trained are in the example program that
> > shows how to use it: http://dlib.net/dnn_face_recognition_ex.cpp.html
> >
> > —
> > You are receiving this because you commented.
> > Reply to this email directly, view it on GitHub
> > <
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> > or unsubscribe
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You could copy the layer parameters into a network with batch norm layers and go in and manually fill in the missing batch norm parameters with something reasonable. |
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Does anyone build the dlib_face_recognition_resnet_model_v1.dat with custom dataset? If Yes, Please share the method. Thanks in Advance. |
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http://dlib.net/dnn_face_recognition_ex.cpp.html and
http://dlib.net/dnn_metric_learning_on_images_ex.cpp.html explain all that.
…On Wed, Sep 9, 2020 at 6:06 AM ismailpolas ***@***.***> wrote:
Does anyone build the dlib_face_recognition_resnet_model_v1.dat with
custom dataset? If Yes, Please share the method. Thanks in Advance.
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I tried Loading a custom dataset and successfully made the resnet .dat file but I'm getting this error. ERROR: Unexpected version found while deserializing dlib::input_rgb_image_sized. Please refer to the issue link given below for more detials on this error. |
How to train my own dlib_face_recognition_resnet_model_v1.dat. Please help and if possible share the python api for the same.
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