Image captioning keras

In this blog, I will present an image captioning model, which generates a realistic caption for an input image.

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To help understand this topic, here are examples:. These two images are random images downloaded from internet, but our model can still generate realistic caption for the image. Our model is trying to understand the objects in the scene and generate a human readable caption. Our code with a writeup are available on Github. Also, we have a short video on YouTube. To train an image captioning model, we used the Flickr30K dataset, which contains 30k images along with five captions for each image.

And we extracted features from the images and save these them as numpy arrays. Then we fed the features into captioning model and got the model trained. Given a new image, we first do a feature extraction, then we feed the features into trained model and get prediction.

Quite straightforward, right? For our baseline, we used GIST to present the images as their features which was arrays with length Then we fed these features into KNN model use ball tree in sklearn :. The training part is quite simple! Then we are going to do prediction.

And we use BallTree to find the K nearest neighbors of A' from which we get all the candidate captions. The last step is deciding which caption we are going to use. Here, we make the decision according to consensus. We use BLEU score in nltk to measure the similarity between two captions, then we choose the caption which maximize the following formula:.

Then we get our prediction! Simple enough! Let's look at something more complicated. For our final, we built our model using Keras, which is a simple wrapper for implementing the building blocks of advanced machine learning algorithms.

To achieve higher performance, we also use GPU. During training, we use VGG for feature extraction, then fed features, captions, mask record previous words and position position of current in the caption into LSTM. The ground truth Y is the next word in the caption. Finally, use a dictionary to interpret the output y into words.

There are two versions of VGG network, 16 layers and 19 layers. We mainly focus on VGG16 which is the 16 layers version. VGG16 network take image with size xx3 3 channel for RGB as input, and return a array as output, indicating which class the object in the image belongs to. Therefore, we need to first resize the image:.

VGG network consists of convolutional layers, pooling layers and full-connected layers. The last three layers are full-connected layers. The last layer is a softmax layer which only tell us which category the image belongs to.

However, the second last layer, fc-2 layer, contains the features of a image as a array.

TensorFlow Tutorial #22 Image Captioning

Therefore, we get our output from the fc-2 layer. The model would look like this:.What do you see in the below picture?

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Definitely all of these captions are relevant for this image and there may be some others also. Even a 5 year old could do this with utmost ease. But, can you write a computer program that takes an image as input and produces a relevant caption as output?

Just prior to the recent development of Deep Neural Networks this problem was inconceivable even by the most advanced researchers in Computer Vision.

But with the advent of Deep Learning this problem can be solved very easily if we have the required dataset. The purpose of this blog post is to explain in as simple words as possible that how Deep Learning can be used to solve this problem of generating a caption for a given image, hence the name Image Captioning.

To get a better feel of this problem, I strongly recommend to use this state-of-the-art system created by Microsoft called as Caption Bot.

Just go to this link and try uploading any picture you want; this system will generate a caption for it. We must first understand how important this problem is to real world scenarios. There are many open source datasets available for this problem, like Flickr 8k containing8k imagesFlickr 30k containing 30k imagesMS COCO containing k imagesetc.

But for the purpose of this case study, I have used the Flickr 8k dataset which you can download by filling this form provided by the University of Illinois at Urbana-Champaign. This dataset contains images each with 5 captions as we have already seen in the Introduction section that an image can have multiple captions, all being relevant simultaneously.

These images are bifurcated as follows:. If you have downloaded the data from the link that I have provided, then, along with images, you will also get some text files related to the images. We can read this file as follows:. The text file looks as follows:.

Image Captioning With AI

For example with reference to the above screenshot the dictionary will look as follows:. The below code does these basic cleaning steps:. This means we have unique words across all the image captions. However, if we think about it, many of these words will occur very few times, say 1, 2 or 3 times. Since we are creating a predictive model, we would not like to have all the words present in our vocabulary but the words which are more likely to occur or which are common.

This helps the model become more robust to outliers and make less mistakes. Hence we consider only those words which occur at least 10 times in the entire corpus.

image captioning keras

The code for this is below:. So now we have only unique words in our vocabulary. However, when we load them, we will add two tokens in every caption as follows significance explained later :.

Images are nothing but input X to our model. As you may already know that any input to a model must be given in the form of a vector. We need to convert every image into a fixed sized vector which can then be fed as input to the neural network. This model was trained on Imagenet dataset to perform image classification on different classes of images.

However, our purpose here is not to classify the image but just get fixed-length informative vector for each image. This process is called automatic feature engineering. Hence, we just remove the last softmax layer from the model and extract a length vector bottleneck features for every image as follows:.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The architecture for the model is inspired from "Show and Tell" [1] by Vinyals et al. The model is built using Keras library. The model is trained on Flickr8k Dataset. The model has been trained for 20 epoches on training samples of Flickr8k Dataset.

After the requirements have been installed, the process from training to testing is fairly easy. The commands to run:. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Image Captioning with Keras. Jupyter Notebook Python. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit e0d62ef Nov 18, Image Captioning Keras Image Captioning System that generates natural language captions for any image.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. May 20, Initial commit. May 10, Added imgs dir. Added more example pics. May 19, Added MIT License. Nov 18, May 12, Some impl changes. May 18, Image captioning is an interesting problem, where you can learn both computer vision techniques and natural language processing techniques.

image captioning keras

This model takes a single image as input and output the caption to this image. For this purpose, I will review the calculation of BLEU by going through its calculation step by step. Flilckr8K contains 8, images that are each paired with five different captions which provide clear descriptions of the salient entities and events.

The images were chosen from six different Flickr groups, and tend not to contain any well-known people or locations, but were manually selected to depict a variety of scenes and situations. The dataset can be downloaded by submitting the request form. The 5 captions for each image share many common words and very similar meaning. Some sentences finish with ". We create a new dataframe dfword to visualize distribution of the words.

It contains each word and its frequency in the entire tokens in decreasing order. To see how these functions work, I will process a single example string using these three functions. ConfigProto config.

image captioning keras

Using TensorFlow backend. The number of jpg flies in Flicker8k: The number of unique file names : The distribution of the number of captions for each image:. Let's have a look at some of the pictures together with the captions. Vocabulary Size: These words do not have much infomation about the data. In order to clean the caption, I will create three functions that: remove punctuation remove single character remove numeric characters To see how these functions work, I will process a single example string using these three functions.

I have python v2. It's pm. Could you buy me iphone7? I ate apples and a banana. Remove punctuations. I ate apples and a banana I have python v27 Its pm Could you buy me iphone7 Remove a single character word.

image captioning keras

After cleaning, the vocabularly size get reduced by about Image captioning is a challenging task at intersection of vision and language. Here, we demonstrate using Keras and eager execution to incorporate an attention mechanism that allows the network to concentrate on image features relevant to the current state of text generation.

In image captioning, an algorithm is given an image and tasked with producing a sensible caption. It is a challenging task for several reasons, not the least being that it involves a notion of saliency or relevance. In this post, we demonstrate a formulation of image captioning as an encoder-decoder problem, enhanced by spatial attention over image grid cells.

The code shown here will work with the current CRAN versions of tensorflowkerasand tfdatasets. The annotations are in JSON format, and there are of them! Depending on your computing environment, you will for sure want to restrict the number of examples used.

Below, we take random samples, split into training and validation parts.

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The companion code will also store the indices on disk, so you can pick up on verification and analysis later. Take, for example, the stereotypical dog vs. What would be salient information to you here? Is the look of the shirt as important as that? You might as well focus on the scenery, - or even something on a completely different level: The age of the photo, or it being an analog one.

What would you say about this scene? Well …. So this is not saying the dataset is biased - not at all. Instead, we want to point out the ambiguities and difficulties inherent in the task. For the encoding part of our encoder-decoder network, we will make use of InceptionV3 to extract image features.

In principle, which features to extract is up to experimentation, - here we just use the last layer before the fully connected top:. The latter shape is what our encoder, soon to be discussed, will receive as input. The original Colab code also shuffles the data on every iteration.

Image Captioning Using Neural Network (CNN & LSTM)

Depending on your hardware, this may take a long time, and given the size of the dataset it is not strictly necessary to get reasonable results. The results reported below were obtained without shuffling. The model is basically the same as that discussed in the machine translation post. Please refer to that article for an explanation of the concepts, as well as a detailed walk-through of the tensor shapes involved at every step.

So you can have a function. The encoder in this case is just a fully connected layer, taking in the features extracted from Inception V3 in flattened form, as they were written to diskand embedding them in dimensional space.

Unlike in the machine translation post, here the attention module is separated out into its own custom model. The logic is the same though:. The decoder at each time step calls the attention module with the features it got from the encoder and its last hidden state, and receives back an attention vector.

The attention vector gets concatenated with the current input and further processed by a GRU and two fully connected layers, the last of which gives us the unnormalized probabilities for the next word in the caption.

The current input at each time step here is the previous word: the correct one during training teacher forcingthe last generated one during inference. We also need to instantiate an optimizer Adam will doand define our loss function categorical crossentropy.When I first started studying ML, I learned as many of us do about classification and regression.

These help us ask and answer questions like: Is this a picture of a cat …. Return to TensorFlow Home. August 07, These help us ask and answer questions like: Is this a picture of a cat or a dog? But, there are other types of questions we might ask, that feel very different.

Can we generate a poem? Text generation Can we generate a photo of a cat? GANs Can we translate a sentence from one language to another? NMT Can we generate a caption for an image? I hope you find them useful, and fun! Eager execution is an imperative, define-by-run interface where operations are executed immediately as they are called from Python.

This makes it easier to get started with TensorFlow, and can make research and development more intuitive. I implemented these examples using Model subclassingwhich allows one to make fully-customizable models by subclassing tf. Model and defining your own forward pass. Model subclassing is particularly useful when eager execution is enabled since the forward pass can be written imperatively.

Each of the examples below is end-to-end, and follows a similar pattern: Automatically download the training data.

Preprocess the training data, and create a tf. Define the model using the tf. Train the model using eager execution. Demonstrate how to use the trained model. Example 1: Text Generation Our first example is for text generation, where we use an RNN to generate text in a similar style to Shakespeare.

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You can run it on Colaboratory with the link above or you can also download it as a Jupyter notebook from GitHub.One application that has really caught the attention of many folks in the space of artificial intelligence is image captioning. If you think about it, there is seemingly no way to tell a bunch of numbers to come up with a caption for an image that accurately describes it.

Now, with the power of deep learning, we can achieve this more accurately than we ever imagined. The problem of writing captions for an image is two-fold: you need to get meaning out of the image by extracting relevant features, and you need to translate these features into a human-readable format. In this tutorial, we're going to look at both phases independently and then connect the pieces together. We'll start with the feature extraction phase.

This tutorial was inspired by the TensorFlow tutorial on image captioning. For this tutorial we are going to use the COCO dataset C ommon O jects in Co ntextwhich consists of over k labelled images, each paired with five captions. For a given image there is always the possibility that there exist redundant elements which barely describe the image in any way.

For instance, a watermark on an image of a horse race tells us virtually nothing about the image itself.

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We need an algorithm that can extract useful features and leave out the redundant ones, like the watermark in this case. Some years ago I probably wouldn't even be writing this tutorial because the methods used for feature extraction required a lot of math and domain-specific expertise. With the emergence of deep learning approaches, feature extraction can now be performed with minimal effort and time, meanwhile achieving more robustness with just a single neural network that has been trained on many images.

But wait, how do we obtain such a vast amount of images to cook up an incredible neural network-based feature extractor? Thanks to transfer learningor the ability to use pre-trained models for inference on new and different problems, we don't need a single image to get started.

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There are many canonical convolutional network models out there that have been trained on millions of images, like ImageNet. All we need to do is slice off the task-specific part of these networks and Bob's your uncle, we have a very robust feature extractor. When we actually dig deeper into the layers of these networks, we observe that each layer is somehow tasked during training to extract specific features.

We therefore have a stack of feature extractors in one network. In this tutorial we're going to use Inception v3, a powerful model developed by Google, as our feature extractor. We can obtain this model with just three lines of Keras code. Since each image is going to have a unique feature representation regardless of the epoch or iteration, it's recommended to run all the images through the feature extractor once and cache the extracted features on disk.

This saves a lot of time since we would not need to perform forward propagation through the feature extractor during each epoch. A summarized workflow of the feature extraction process is as follows:. This phase also uses a neural network, specifically a Recurrent Neural Network RNN retrofitted with some mechanisms to increase robustness to translating features to language.

I must confess that this is the most tedious part, but we're going to keep it simple and straightforward. The first thing we're going to do here is to process our text dataset in four simple steps:.

Earlier I mentioned that our model is going to be enhanced with an attention mechanism. The whole concept of the attention mechanism is really intuitive.

One popular field where the attention mechanism has become really prevalent is Neural Machine Translation, and the idea behind the attention mechanism in Machine Translation is quite similar to that in image captioning. If you want to learn more about how the attention mechanism works, or if you have any difficulty understanding the code below, check out my previous post on Neural Machine Translation.

The output of the linear layer is what we feed into our recurrent network retrofitted with the attention mechanism.


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