How to Build a Multimodal Text and Image Classification

Text and image classification models (Multimodal) are a type of machine learning model that can classify images based on their content and text descriptions. These models are useful in a wide range of applications such as image search, content recommendation, and more. In this post, we will explore how to build a text and image classification model using PyTorch and TensorFlow. We will cover the steps involved in data preparation, text and image embedding, model architecture, training, and evaluation. We will also provide code examples in PyTorch and TensorFlow to help you get started. By the end of this post, you will have a good understanding of how to build a text and image classification model and how to apply it to your own projects.

How to Implement a Text and Image Classification Model

1. Data Preparation: Collect a dataset of images and their corresponding text descriptions. Preprocess the images and text to ensure they are in a format that can be used by the model.

2. Text Embedding: Use a pre-trained text embedding model such as BERT or GloVe to convert the text descriptions into a vector representation.

3. Image Embedding: Use a pre-trained image embedding model such as ResNet or VGG to convert the images into a vector representation.

4. Model Architecture: Combine the text and image embeddings using a fusion layer such as concatenation or element-wise multiplication. Pass the fused embeddings through a classification layer such as a fully connected layer to obtain the final classification.

5. Training: Train the model on the dataset using a suitable loss function such as cross-entropy loss. Use techniques such as data augmentation and regularization to prevent overfitting.

6. Evaluation: Evaluate the performance of the model on a held-out test set using metrics such as accuracy, precision, recall, and F1 score.

Implementation

1. Data Preparation: Collect a dataset of images and their corresponding text descriptions. Preprocess the images and text to ensure they are in a format that can be used by the model.

import torch

from torch.utils.data import Dataset, DataLoader

from torchvision import transforms

from PIL import Image



class ImageTextDataset(Dataset):

    def __init__(self, image_paths, text_descriptions, labels, transform=None):

        self.image_paths = image_paths

        self.text_descriptions = text_descriptions

        self.labels = labels

        self.transform = transform



    def __len__(self):

        return len(self.image_paths)



    def __getitem__(self, idx):

        image_path = self.image_paths[idx]

        text_description = self.text_descriptions[idx]

        label = self.labels[idx]



        image = Image.open(image_path).convert('RGB')

        if self.transform:

            image = self.transform(image)



        return image, text_description, label



transform = transforms.Compose([

    transforms.Resize(256),

    transforms.CenterCrop(224),

    transforms.ToTensor(),

    transforms.Normalize(mean=[0.485, 0.456, 0.406],

                         std=[0.229, 0.224, 0.225])

])



dataset = ImageTextDataset(image_paths, text_descriptions, labels, transform=transform)

dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

2. Text Embedding: Use a pre-trained text embedding model such as BERT or GloVe to convert the text descriptions into a vector representation.

import torch.nn as nn
from transformers import BertModel

class TextEncoder(nn.Module):
    def __init__(self):
        super(TextEncoder, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')

    def forward(self, input_ids, attention_mask):
        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = outputs[1]
        return pooled_output

3. Image Embedding: Use a pre-trained image embedding model such as ResNet or VGG to convert the images into a vector representation.

import torch.nn as nn
import torchvision.models as models

class ImageEncoder(nn.Module):
    def __init__(self):
        super(ImageEncoder, self).__init__()
        self.resnet = models.resnet50(pretrained=True)
        self.resnet.fc = nn.Identity()

    def forward(self, x):
        features = self.resnet(x)
        return features

4. Model Architecture: Combine the text and image embeddings using a fusion layer such as concatenation or element-wise multiplication. Pass the fused embeddings through a classification layer such as a fully connected layer to obtain the final classification.

import torch.nn as nn

class ImageTextClassifier(nn.Module):
    def __init__(self, num_classes):
        super(ImageTextClassifier, self).__init__()
        self.text_encoder = TextEncoder()
        self.image_encoder = ImageEncoder()
        self.fusion_layer = nn.Linear(768 + 2048, 512)
        self.classifier = nn.Linear(512, num_classes)

    def forward(self, images, input_ids, attention_mask):
        image_features = self.image_encoder(images)
        text_features = self.text_encoder(input_ids, attention_mask)
        fused_features = torch.cat((image_features, text_features), dim=1)
        fused_features = self.fusion_layer(fused_features)
        logits = self.classifier(fused_features)
        return logits

5. Training: Train the model on the dataset using a suitable loss function such as cross-entropy loss. Use techniques such as data augmentation and regularization to prevent overfitting.

import torch.optim as optim
import torch.nn.functional as F

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = ImageTextClassifier(num_classes).to(device)
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

for epoch in range(num_epochs):
    running_loss = 0.0
    for i, data in enumerate(dataloader, 0):
        images, text_descriptions, labels = data
        images = images.to(device)
        input_ids, attention_mask = tokenizer.batch_encode_plus(text_descriptions, padding=True, return_tensors='pt').values()
        input_ids = input_ids.to(device)
        attention_mask = attention_mask.to(device)
        labels = labels.to(device)

        optimizer.zero_grad()

        outputs = model(images, input_ids, attention_mask)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()

    print(f'Epoch {epoch + 1}/{num_epochs}, Loss: {running_loss / len(dataloader):.4f}')
# Save the model checkpoint
    torch.save({
        'epoch': epoch,
        'model_state_dict': model.state_dict(),
        'optimizer_state_dict': optimizer.state_dict(),
        'loss': loss,
    }, 'model_checkpoint.pth')