Image captioning is the process of generating a textual description of an image. In this blog post, we will be discussing how to train a deep learning model for image captioning.
For more information check out our video:
In this tutorial, you will learn how to use a Convolutional Neural Network (CNN) for image captioning. We’ll then use a recurrent neural network (LSTM) to generate captions for new images!
What is Deep Learning?
Deep learning is a branch of machine learning that deals with algorithms inspired by the structure and function of the brain. These are called artificial neural networks (ANNs). Deep learning algorithms learn from data in a way that is similar to how our brains learn from data.
Deep learning is used for image captioning, which is a task that involves generating textual descriptions of images. deep learning algorithms have been able to achieve state-of-the-art performance on this task.
In this project tutorial, we will be using a deep learning algorithm to caption images. We will be using the TensorFlow library for this purpose. TensorFlow is a library that was developed by Google for use in their own machine learning projects.
What is Image Captioning?
Image captioning is the task of generating a textual description of an image. It requires systems to jointly learn to interpret images and natural language. For example, given the following image as input:
![Image of Mosque](https://i.imgur.com/JDL8lWy.png)
The expected output would be a sentence such as “a group of people standing in front of a large building.”
Image captioning is a popular topic in computer vision and deep learning. In this tutorial, you will learn how to use a convolutional neural network (CNN) to generate captions for images.
Why Use Deep Learning for Image Captioning?
Deep learning is a powerful tool for image captioning because it can learn complex relationships between image data and textual data. For example, deep learning can automatically learn to caption an image of a dog with a Text-based description such as “A brown and white dog is running across a field.”
How to Implement Image Captioning with Deep Learning
Image captioning is a process of automatically generating a textual description of an image. It involves using a deep learning model to generate captions for an image.
In this project tutorial, we will be implementing an image captioning model using Deep Learning. We will be using the TensorFlow library for our implementation.
Dataset Used for Image Captioning
In order to train our image captioning model, we need a dataset of images and their corresponding captions. The dataset that we will be using is the Microsoft COCO dataset. The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset. The dataset contains over 82,000 images, each of which has at least 5 captions associated with it.
The images in the COCO dataset are divided into 3 types: train, val, and test. The training set contains approximately 75% of the images in the COCO dataset, while the val and test sets contain 12.5% and 12.5% respectively. For this project, we will only be using the training set of images.
Architecture of the Image Captioning Model
In this section, we’ll briefly describe the architecture of the image captioning model that we’ll be using. The model consists of two main components: a convolutional neural network (CNN) for feature extraction, and a recurrent neural network (RNN) for encoding the features into natural language.
The CNN takes an image as input and outputs a set of features that represent the content of the image. These features are then fed into the RNN, which encodes them into a caption.
![Image Captioning Model](https://github.com/karenfu/image-captioning/raw/master/assets/model.png)
The image captioning model can be trained end-to-end, meaning that we can train it to automatically learn to map images to captions. However, in practice, it is often easier to pre-train the CNN on a large dataset of images (e.g. ImageNet), and then fine-tune the CNN on a smaller dataset of images with captions (e.g. Flickr8k). This is because training a CNN from scratch requires a lot of data and computational power, whereas fine-tuning a pretrained CNN is relatively fast and easy.
Training the Image Captioning Model
In this section, we will train our image captioning model. We will start by pre-processing the images and then we will train the model on our training set.
First, we need to pre-process the images so that they can be fed into our convolutional neural network (CNN). We will resize the images to 299×299 pixels as this is the input size that our CNN expects. We will also convert the images from RGB to BGR as the Inception model that we are using was trained on images in the BGR format. Finally, we will rescale the images so that they have pixel values between -1 and 1.
Training the Model
Now that our images are ready, we can start training our model. We will first load the InceptionV3 model which we will use as a base for our image captioning model. We will then add a LSTM layer followed by a fully connected layer and a softmax activation layer. We will also initialize the InceptionV3 model with weights from Imagenet.
Evaluating the Image Captioning Model
After training our image captioning model, it’s time to evaluate it on new data. In this section, we’ll see how to evaluate the performance of our model by captioning a number of images from the Flickr8k testing dataset.
First, let’s load the necessary packages. We’ll be using the Keras Python library for deep learning.
import numpy as np
import matplotlib.pyplot as plt
from keras.preprocessing import image
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input
from keras.preprocessing import sequence
from keras.models import load_model
Using TensorFlow backend.
(1) IndexError Traceback (most recent call last)
1 # Let’s define a new Model that will take an image as input, and will output
—-> 2 # a set of predicted words about the image:
4 #Model 3 – VGG 16 test write up and conclusions
5 def get_predicted_caption(image_path):
IndexError: index 1 is out of bounds for axis 0 with size 1
In this image captioning with deep learning project tutorial, we learned how to use a pre-trained deep learning model to generate captions for images. We also learned how to fine-tune a pre-trained model to improve the performance of the captioning model on a new dataset.
Keyword: Image Captioning with Deep Learning: A Project Tutorial