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Caption Generation Using PyTorch

In this project, we will build a caption generator for images using ConvNeXt and Transformer models. We will be using PyTorch as a deep learning framework that makes it easy to write and train neural networks.

Caption Generation Using PyTorch

You will learn to:

Load and manage the image datasets.

Create and use pretrained models in PyTorch.

Improve the models by performing error analysis.

Create an interface to run the model.


Deep Learning

Transformer Models

Generative AI


Basic understanding of Python

Basic understanding of PyTorch module of Python

Knowledge of deep learning architectures, ResNet, and Transformers

Deep learning concepts such as pretraining and fine-tuning




Project Description

This project aims to develop a captions generator for images using ConvNeXt and Transformer models. We will use pretrained models to fine-tune the Flickr8k dataset. We’ll use PyTorch as a deep learning framework. We’ll start with a basic introduction to PyTorch, get comfortable with the dataset, and build, evaluate, and deploy the model. Ultimately, we’ll create an interface to use the model for caption generation. 

We’ll work with two of the most famous deep learning architectures: ConvNeXt and Transformer. ConvNeXt is a recent convolutional network architecture that was designed by using the design decisions of Vision Transformer into a convolutional network. ConvNeXt achieved state-of-the-art results on tasks such as classification, detection, and segmentation. Transformer, a neural network architecture, has reshaped the field of natural language processing and impacted areas such as vision and audio. It employs self-attention mechanisms to capture contextual relationships in data.

Project Tasks


Getting Started

Task 0: Get Started

Task 1: Import Libraries

Task 2: Set Random Seed


Prepare the Dataset

Task 3: Load and Visualize the Data

Task 4: Create a Transformation Function

Task 5: Create a Tokenizer

Task 6: Transform the Dataset

Task 7: Create a DataLoader


Create the Model

Task 8: Prepare ConvNeXt Model

Task 9: Create a Model Using ConvNeXt and Transformer Decoder

Task 10: Set the Loss function

Task 11: Set the Optimizer

Task 12: Train the Model


Evaluate and Deploy the Model

Task 13: Load the Pretrained Model

Task 14: Evaluate the Model

Task 15: Deploy the Model Using Gradio