safrooze commented on a change in pull request #10959: [MXNET-423] Gluon Model
Zoo Pre Trained Model tutorial
File path: docs/tutorials/gluon/pretrained_models.md
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+# 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
+* **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
+* **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.
+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
+## Loading the model
+The [Gluon Model
provides a collection of off-the-shelf models. You can get the ImageNet
pre-trained model by using `pretrained=True`.
+If you want to train on your own classification problem from scratch, you can
get an untrained network with a specific number of classes using the `classes`
parameter: for example `classes=10`.
Maybe add a line of code to show how to get the untrained model with
different number of classes? Also perhaps emphasise that one cannot change
number of classes and user pretrained as the same time.
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