safrooze commented on a change in pull request #10959: [MXNET-423] Gluon Model Zoo Pre Trained Model tutorial URL: https://github.com/apache/incubator-mxnet/pull/10959#discussion_r189034610
########## File path: docs/tutorials/gluon/pretrained_models.md ########## @@ -0,0 +1,373 @@ + +# Using pre-trained models in MXNet + +In this tutorial we will see how to use multiple pre-trained models with Apache MXNet. First, let's download three images classification models from the Apache MXNet [Gluon model zoo](https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html). +* **DenseNet-121** ([research paper](https://arxiv.org/abs/1608.06993)), improved state of the art on [ImageNet dataset](http://image-net.org/challenges/LSVRC) in 2016. +* **MobileNet** ([research paper](https://arxiv.org/abs/1704.04861)), MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks, suitable for mobile applications. +* **ResNet-18** ([research paper](https://arxiv.org/abs/1512.03385v1)), the -152 version is the 2015 winner in multiple categories. + +Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the tutorial, even though these models have been trained on the same dataset and optimized for maximum accuracy, they do behave slightly differently on specific images. In addition, prediction speed and memory footprints can vary, and that's an important factor for many applications. By trying a few pretrained models, you have an opportunity to find a model that can be a good fit for solving your business problem. + + +```python +import mxnet as mx +from mxnet import gluon, nd +from mxnet.gluon.model_zoo import vision +import matplotlib.pyplot as plt +import numpy as np +import json Review comment: PEP8: system imports (json) before other packages followed by a blank line. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
