Introduction
In the world of computer vision, one common task is to identify and localize objects within images. In this project, we delve into the fascinating realm of object localization by building a model that predicts bounding boxes around human torsos in images. Our dataset of choice for this endeavor is the Frames Labeled In Cinema (FLIC) dataset, which provides us with the necessary images and corresponding bounding box annotations.
But how do we go about building such a model? Join us on this journey as we break down the process step by step, from data preparation to model evaluation.
Data Preparation
To kick things off, we load our dataset and preprocess the images. The FLIC dataset comes with a CSV file containing image filenames and their corresponding bounding box coordinates. We read this file into a Pandas DataFrame and resize the images to a consistent size of 128x128 pixels. Proper data preprocessing is key to setting the stage for our model.
Data Augmentation
Data augmentation plays a pivotal role in improving model generalization. By using the Albumentations library, we apply transformations like horizontal and vertical flips to our images and update the bounding box coordinates accordingly. This augments our dataset, providing our model with a broader range of training examples.
Dataset Splitting
Once we've prepared and augmented our data, it's time to split it into training, validation, and testing sets. We use the train_test_split function from Scikit-Learn to achieve this. Proper dataset splitting ensures that our model learns effectively and generalizes well to unseen data.
Model Building
Our model is built on the foundation of Convolutional Neural Networks (CNNs), a powerful architecture for image-related tasks. Our CNN architecture comprises convolutional layers for feature extraction, max-pooling layers for downsampling, and dense layers for prediction. The output layer consists of four units corresponding to the coordinates of the bounding box.
Model Training
Training a deep learning model requires careful consideration. We compile our model with an Adam optimizer and mean squared error loss. Additionally, we implement early stopping to prevent overfitting and save the best model checkpoint.
Model Evaluation
Evaluating our model's performance is a crucial step. We load the best model checkpoint and evaluate it on the test dataset. The mean squared error is our chosen metric to measure how well the model predicts bounding box coordinates.
Conclusion
In this project, we embarked on a journey to predict torso bounding boxes in images. We've covered the entire process, from data preparation and augmentation to model building, training, and evaluation. This project serves as a stepping stone into the exciting world of computer vision and deep learning.
But our exploration doesn't end here. There are countless other avenues to explore within this field, from object detection and segmentation to more advanced architectures and datasets. We're only scratching the surface, and the possibilities are limitless.
Written By: Naman Shah (Intern)
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