indhub commented on a change in pull request #10956: [MXNET-307] Fix flaky tutorial tests from CI URL: https://github.com/apache/incubator-mxnet/pull/10956#discussion_r188796904
########## File path: docs/tutorials/python/predict_image.md ########## @@ -1,33 +1,28 @@ # Predict with pre-trained models -This tutorial explains how to recognize objects in an image with a -pre-trained model, and how to perform feature extraction. +This tutorial explains how to recognize objects in an image with a pre-trained model, and how to perform feature extraction. ## Prerequisites To complete this tutorial, we need: - MXNet. See the instructions for your operating system in [Setup and Installation](http://mxnet.io/install/index.html) -- [Python Requests](http://docs.python-requests.org/en/master/), [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html). +- [Matplotlib](https://matplotlib.org/) and [Jupyter Notebook](http://jupyter.org/index.html). ``` -$ pip install requests matplotlib jupyter opencv-python +$ pip install matplotlib ``` ## Loading -We first download a pre-trained ResNet 152 layer that is trained on the full -ImageNet dataset with over 10 million images and 10 thousand classes. A -pre-trained model contains two parts, a json file containing the model -definition and a binary file containing the parameters. In addition, there may be -a text file for the labels. +We first download a pre-trained ResNet 18 layer that is trained on the ImageNet dataset with over 1 million images and one thousand classes. A pre-trained model contains two parts, a json file containing the model definition and a binary file containing the parameters. In addition, there may be a `synset.txt` text file for the labels. Review comment: 'ResNet 18 model' or 'ResNet 18 layer model' ---------------------------------------------------------------- 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
