Compare deep learning framework between TensorFlow and PaddlePaddle

Question

I want to study on the research of deep learning, but I don't know which framwork should I choice between TensorFlow and PaddlePaddle. who can make a contrast between the two frameworks? which one is better? especially in the running efficiency of CPU

Answer 1

It really depends what you are shooting for... If you plan on training, CPU is not going to work well for you. Use colab or kaggle.

Assuming you do get a GPU, it depends if you want to focus on classification or object detection.

If you focus on classification, Keras is probably the easiest to work with or pytorch if you want some advanced stuff and to be able to change things.

If you plan on object detection, things are getting complicated... Inference is reasonably easy but training is complicated. There are actually 4 platforms you should consider:

1. Tensorflow - powerful but very difficult to work with. If you do not use Keras (and for OD you usually can't), you need to preprocess the dataset into tfrecords and it is a pain. The OD Api has very cryptic messages and it is very sensitive to the combination of tf version and api version. On the other hand, cool models like efficientdet are more or less easy to use.

2. MMdetection - very powerful framework, has lots of advanced models and once you understand how to work with it, you can easily work with and of the models it supports. Downside is that some models are slow to arrive (efficientdet, for example)

3. paddlepaddle - if you know Chinese, this should work ok, maybe. The documentation is a bit behind and usually requires lots of improvisation. Basically it is similar to mmdetection just with a few unique models and a few missing models.

4. detectron2 - I didn't work with this one, but it seems to support only a few models.

You probably need first to define for yourself what do you want to do and then choose.

Good luck!

Answer 2

It is not that trivial. Some models run faster with one kind of framework others with another. Furthermore, it depends on the hardware as well. See this blog. If inference is your only concern, then you can develop your model in any of the popular frameworks like TensorFlow, PyTorch, etc. In the end convert your model to ONNX format and benchmark its performance with DNN-Bench to choose the best inference engine for your application.