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.
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.
Users' choice of different deep learning frameworks is a personal choice, and
the reason why most of us chose paddle is because of its better support for
embedded development and different hardware platforms and because the
community is very active and has proposed industrial improvements and
implementations for some advanced models. Especially for the GPU, it supports
cuda and opencl, which means we can optimize the model no matter what kind of
graphics card is used. In my opinion, more backend support can better improve
dnn libavfilter modules.
If there are any new changes in dnn libavfilter module, I will be very willing
to adjust our implementation with the new planning and provide continuous
maintenance.
Best Regards,
Wenzhe Wang
WenzheWang
wong...@foxmail.com
------------------ Original ------------------
From:
"WenzheWang"
<wong...@foxmail.com>;
Date: Tue, Apr 11, 2023 11:03 PM
To: "ffmpeg-devel"<ffmpeg-devel@ffmpeg.org>;
Subject: Re: [FFmpeg-devel] [PATCH v1] libavfi/dnn: add Paddle Inference
as one of DNN backend
Could you please briefly introduce the reason why not adding any dnn
backend?
Do you have any plan for the maintenance and development of the dnn backend in
the future? From my understanding, the current backend of dnn has tensoflow,
openvino and native, but this cannot meet the needs of users.
Thus, I believe adding other dnn backends will be great for user experience,
user growth, and industrial applications. In particular, various dnn backend
can be adapted to different application environments, and there are some
emerging inference engines that are faster and stronger, such as Pytorch and
Paddle. In addition, from the practical point of view, it is not difficult for
a deep learning practitioner to learn and use this framework, but how to choose
a framework and apply it in practice, people pay more attention to the effect
(recall and precision), and easy deployment, that is, high reasoning
performance efficiency. The main reason why Paddle is relatively mainstream and
why I want to add paddle backend is that it has a very high efficiency and
performance. There are several projects maintained by Paddle, such as
paddleDetection, paddleSeg, paddleGAN, paddleOCR and paddleCls have a lot of
good pre-training models that migrate well to their own data and has excellent
perform
ance. Secondly, in terms of reasoning efficiency, Paddle supports many
platforms and chips. Models trained using Paddle framework can be directly
deployed, and custom device interfaces are open for independent development
based on one's own hardware.
FFmpeg itself already has very extensive support for codec. If FFmpeg could
support the deployment of more reasoning model backend, it would have a wider
application.
In general, I hope that ffmpeg could support the backend of paddle or more. In
any case that my code is not mature or proper, I would be grateful if
professionals like you could offer me suggestions and comments. I will be
absolutely honored if I could contribute to this project :)
Best,
Wenzhe Wang
WenzheWang
wong...@foxmail.com
------------------ Original ------------------
From:
"FFmpeg development discussions
and patches"
<j...@videolan.org>;
Date: Sun, Apr 9, 2023 05:31 AM
To: "ffmpeg-devel"<ffmpeg-devel@ffmpeg.org>;
Subject: Re: [FFmpeg-devel] [PATCH v1] libavfi/dnn: add Paddle Inference
as one of DNN backend
On Thu, 6 Apr 2023, at 12:36, wong...@foxmail.com wrote:
> PaddlePaddle (PArallel Distributed Deep LEarning) is a simple,
> efficient and extensible deep learning framework that accelerates the
Please don't add another DNN backend.
--
Jean-Baptiste Kempf - President
+33 672 704 734
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