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Assignment #3: Image Captioning with Vanilla RNNs
Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. You will also explore methods for visualizing the features of a pretrained model on ImageNet, and also this model to implement Style Transfer. Finally, you will train a Generative Adversarial Network to generate images that look like a training dataset!
The goals of this assignment are as follows:
Understand the architecture of recurrent neural networks (RNNs) and how they operate on sequences by sharing weights over time
Understand and implement both Vanilla RNNs and Long-Short Term Memory (LSTM) networks.
Understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system
Explore various applications of image gradients, including saliency maps, fooling images, class visualizations.
Understand and implement techniques for image style transfer.
Understand how to train and implement a Generative Adversarial Network (GAN) to produce images that resemble samples from a dataset.
Setup
Get the code as a zip file here.
Update (5/11/18, 6:05 a.m.): The assignment zip file has been updated to include the proper optim.py and layers.py files.
You can follow the setup instructions here.
If you haven’t already, you’ll need to install either TensorFlow 1.7 (installation instructions here) or PyTorch 0.4 (instructions here) depending on which notebooks you decide to complete.
Download data:
Once you have the starter code, you will need to download the COCO captioning data, pretrained SqueezeNet model (TensorFlow-only), and a few ImageNet validation images. Run the following from the assignment3directory:
cd cs231n/datasets
./get_assignment3_data.sh
Start IPython:
After you have downloaded the data, you should start the IPython notebook server from the assignment3directory, with the jupyter notebook command. (See the Google Cloud Tutorial for any additional steps you may need to do for setting this up, if you are working remotely)
If you are unfamiliar with IPython, you can also refer to our IPython tutorial.
Some Notes
NOTE 1: This year, the assignment3 code has been tested to be compatible with python version 3.6 (it may work with other versions of 3.x, but we won’t be officially supporting them). You will need to make sure that during your virtual environment setup that the correct version of python is used. You can confirm your python version by (1) activating your virtualenv and (2) running which python.
NOTE 2: If you are working in a virtual environment on OSX, you may potentially encounter errors with matplotlib due to the issues described here. In our testing, it seems that this issue is no longer present with the most recent version of matplotlib, but if you do end up running into this issue you may have to use the start_ipython_osx.shscript from the assignment3 directory (instead of jupyter notebook above) to launch your IPython notebook server. Note that you may have to modify some variables within the script to match your version of python/installation directory. The script assumes that your virtual environment is named .env.
You can do Questions 3, 4, and 5 in TensorFlow or PyTorch. There are two versions of each of these notebooks, one for TensorFlow and one for PyTorch. No extra credit will be awarded if you do a question in both TensorFlow and PyTorch.
Q1: Image Captioning with Vanilla RNNs (25 points)
The Jupyter notebook RNN_Captioning.ipynb will walk you through the implementation of an image captioning system on MS-COCO using vanilla recurrent networks.
Q2: Image Captioning with LSTMs (30 points)
The Jupyter notebook LSTM_Captioning.ipynb will walk you through the implementation of Long-Short Term Memory (LSTM) RNNs, and apply them to image captioning on MS-COCO.
Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)
The Jupyter notebooks NetworkVisualization-TensorFlow.ipynb /NetworkVisualization-PyTorch.ipynbwill introduce the pretrained SqueezeNet model, compute gradients with respect to images, and use them to produce saliency maps and fooling images. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
Q4: Style Transfer (15 points)
In the Jupyter notebooks StyleTransfer-TensorFlow.ipynb/StyleTransfer-PyTorch.ipynb you will learn how to create images with the content of one image but the style of another. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
Q5: Generative Adversarial Networks (15 points)
In the Jupyter notebooks GANS-TensorFlow.ipynb/GANS-PyTorch.ipynb you will learn how to generate images that match a training dataset, and use these models to improve classifier performance when training on a large amount of unlabeled data and a small amount of labeled data. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awarded if you complete both notebooks.
In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. You will also explore methods for visualizing the features of a pretrained model on ImageNet, and also this model to implement Style Transfer. Finally, you will train a Generative Adversarial Network to generate images that look like a training dataset!
The goals of this assignment are as follows:
Understand the architecture of recurrent neural networks (RNNs) and how they operate on sequences by sharing weights over time
Understand and implement both Vanilla RNNs and Long-Short Term Memory (LSTM) networks.
Understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system
Explore various applications of image gradients, including saliency maps, fooling images, class visualizations.
Understand and implement techniques for image style transfer.
Understand how to train and implement a Generative Adversarial Network (GAN) to produce images that resemble samples from a dataset.
Setup
Get the code as a zip file here.
Update (5/11/18, 6:05 a.m.): The assignment zip file has been updated to include the proper optim.py and layers.py files.
You can follow the setup instructions here.
If you haven’t already, you’ll need to install either TensorFlow 1.7 (installation instructions here) or PyTorch 0.4 (instructions here) depending on which notebooks you decide to complete.
Download data:
Once you have the starter code, you will need to download the COCO captioning data, pretrained SqueezeNet model (TensorFlow-only), and a few ImageNet validation images. Run the following from the assignment3directory:
cd cs231n/datasets
./get_assignment3_data.sh
Start IPython:
After you have downloaded the data, you should start the IPython notebook server from the assignment3directory, with the jupyter notebook command. (See the Google Cloud Tutorial for any additional steps you may need to do for setting this up, if you are working remotely)
If you are unfamiliar with IPython, you can also refer to our IPython tutorial.
Some Notes
NOTE 1: This year, the assignment3 code has been tested to be compatible with python version 3.6 (it may work with other versions of 3.x, but we won’t be officially supporting them). You will need to make sure that during your virtual environment setup that the correct version of python is used. You can confirm your python version by (1) activating your virtualenv and (2) running which python.
NOTE 2: If you are working in a virtual environment on OSX, you may potentially encounter errors with matplotlib due to the issues described here. In our testing, it seems that this issue is no longer present with the most recent version of matplotlib, but if you do end up running into this issue you may have to use the start_ipython_osx.shscript from the assignment3 directory (instead of jupyter notebook above) to launch your IPython notebook server. Note that you may have to modify some variables within the script to match your version of python/installation directory. The script assumes that your virtual environment is named .env.
You can do Questions 3, 4, and 5 in TensorFlow or PyTorch. There are two versions of each of these notebooks, one for TensorFlow and one for PyTorch. No extra credit will be awarded if you do a question in both TensorFlow and PyTorch.
Q1: Image Captioning with Vanilla RNNs (25 points)
The Jupyter notebook RNN_Captioning.ipynb will walk you through the implementation of an image captioning system on MS-COCO using vanilla recurrent networks.
Q2: Image Captioning with LSTMs (30 points)
The Jupyter notebook LSTM_Captioning.ipynb will walk you through the implementation of Long-Short Term Memory (LSTM) RNNs, and apply them to image captioning on MS-COCO.
Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)
The Jupyter notebooks NetworkVisualization-TensorFlow.ipynb /NetworkVisualization-PyTorch.ipynbwill introduce the pretrained SqueezeNet model, compute gradients with respect to images, and use them to produce saliency maps and fooling images. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
Q4: Style Transfer (15 points)
In the Jupyter notebooks StyleTransfer-TensorFlow.ipynb/StyleTransfer-PyTorch.ipynb you will learn how to create images with the content of one image but the style of another. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awardeded if you complete both notebooks.
Q5: Generative Adversarial Networks (15 points)
In the Jupyter notebooks GANS-TensorFlow.ipynb/GANS-PyTorch.ipynb you will learn how to generate images that match a training dataset, and use these models to improve classifier performance when training on a large amount of unlabeled data and a small amount of labeled data. Please complete only one of the notebooks (TensorFlow or PyTorch). No extra credit will be awarded if you complete both notebooks.
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