Thank you for your reply and I am very glad to receive your opinion.
For me,github star is not very important, I just want to use it to highlight
how many people are using deep learning frameworks right now. However, I have
learned about dnn's plans for other frameworks.
I think it would be a good idea to add a glue layer and hope to see it
implemented soon. Do I have the opportunity to participate in the development
of this module?
Thank you again for answering my question.
best,
Wenzhe
WenzheWang
wong...@foxmail.com
------------------ ???????? ------------------
??????:
"FFmpeg development discussions
and patches"
<yejun.guo-at-intel....@ffmpeg.org>;
????????: 2023??5??10??(??????) ????7:30
??????: "FFmpeg development discussions and
patches"<ffmpeg-devel@ffmpeg.org>;
????: Re: [FFmpeg-devel] [PATCH v1] libavfi/dnn: add Paddle Inference as
one of DNN backend
> -----Original Message-----
> From: ffmpeg-devel <ffmpeg-devel-boun...@ffmpeg.org> On Behalf Of
> "zhilizhao(??????)"
> Sent: Wednesday, May 10, 2023 12:09 PM
> To: FFmpeg development discussions and patches <ffmpeg-
> de...@ffmpeg.org>
> Subject: Re: [FFmpeg-devel] [PATCH v1] libavfi/dnn: add Paddle Inference as
> one of DNN backend
>
>
>
> > On May 10, 2023, at 10:25, WenzheWang <wong...@foxmail.com> wrote:
> >
> > Dear Madam or Sir,
> >
> >
> > Hope this email finds you well.
> >
> >
> > I am writing this email since i recently found FFmepg remove DNN
native
> backend, and i will be really grateful if you let me know if there
is any new
> plan on libavfilter/dnn.
> >
> >
> > I would like to explain to you again about the addition of dnn paddle
> backend.
> >
> > At present, ffmpeg only supports openvino and tensorflow
backend.
> Among the current deep learning frameworks, TensorFlow is the most
active
> in development. TensorFlow has 174k stars and pytorch has 66.5k.
openvino
> is 4.2k, and the models that openvino can implement are relatively
few. But
> in terms of attention on GitHub, there's no doubt that TensorFlow and
> pytorch are more promising. Currently, the paddle framework has
reached
> 20.2k stars on github, which is much more widely used and active than
> frameworks such as mxnet and caffe.
>
> Stars don't matter much here.
>
> Just for reference, there is a thread before:
>
> https://patchwork.ffmpeg.org/project/ffmpeg/patch/20220523092918.9548-
> 2-ting...@intel.com/
>
> >
> > Tensoflow has a very rich ecosystem. The TensorFlow models
library
> updates very quickly and has existing examples of deep learning
applications
> for image classification, object detection, image generation text,
and
> generation of adversus-network models. The dnn libavfilter module is
> undoubtedly very necessary for tensorflow backend to support. But the
> complexity of the TensorFlow API and the complexity of the training
are
> almost prohibitive, making it a love-hate framework.
> >
> > PyTorch framework tends to be applied to academic fast
implementation,
> and its industrial application performance is not good. For example,
Pytorch
> framework makes a model to run on a server, Android phone or embedded
> system, and its performance is poor compared with other deep learning
> frameworks.
> >
> >
> > PaddlePadddle is an open source framework of Baidu, which is
also used
> by many people in China. It is very consistent with the usage habits
of
> developers, but the practicability of the API still needs to be
further
> strengthened. However, Paddle is the only deep learning framework I
have
> ever used, which does not configure any third-party libraries and
can be
> used directly by cloning make. Besides, Paddle occupies a small
amount of
> memory and is fast. It also serves a considerable number of projects
inside
> Baidu, which is very strong in industrial application. And
PaddlePaddle
> supports multiple machine and multiple card training.
Imo, my idea is that we can add 1 or 2 dnn backends as discussed at
http://ffmpeg.org/pipermail/ffmpeg-devel/2022-December/304534.html
The background is that we see different good models from different deep learning
frameworks, and most framework does not support models developed with other
frameworks due to different model format. imo, we'd support several popular
frameworks.
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