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&gt;;
Date:&nbsp;Tue, Apr 11, 2023 11:03 PM
To:&nbsp;"ffmpeg-devel"<ffmpeg-devel@ffmpeg.org&gt;;

Subject:&nbsp;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?&nbsp;




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



&nbsp;




------------------ Original ------------------
From:                                                                           
                                             "FFmpeg development discussions 
and patches"                                                                    
                <j...@videolan.org&gt;;
Date:&nbsp;Sun, Apr 9, 2023 05:31 AM
To:&nbsp;"ffmpeg-devel"<ffmpeg-devel@ffmpeg.org&gt;;

Subject:&nbsp;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:
&gt; PaddlePaddle (PArallel Distributed Deep LEarning) is a simple, 
&gt; efficient and extensible deep learning framework that accelerates the 

Please don't add another DNN backend.

-- 
Jean-Baptiste Kempf -&nbsp; President
+33 672 704 734
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