Building a Custom Image Classification Model with Hugging Face Transformers on Your Laptop

Introduction

The world of computer vision has witnessed tremendous growth in recent years, thanks to the advancements in deep learning and transformer-based architectures. In this article, we will explore the process of building a custom image classification model using Hugging Face Transformers on your laptop. We’ll delve into the theoretical aspects, provide practical examples, and cover the essential steps required for this endeavor.

What is Image Classification?

Image classification is a fundamental task in computer vision where images are categorized into predefined classes or labels. This task has numerous applications in areas like object detection, facial recognition, and autonomous vehicles.

Why Hugging Face Transformers?

Hugging Face Transformers have revolutionized the field of natural language processing (NLP) but have also been successfully applied to image classification tasks. Their ability to handle sequential data makes them suitable for modeling complex relationships between pixels. In this article, we’ll focus on leveraging the capabilities of Transformers in the context of image classification.

Prerequisites

Before diving into the tutorial, ensure you have the following installed on your laptop:

Python 3.8 or later
CUDA/ cuDNN (for NVIDIA GPUs)
Torch and transformers libraries

Step 1: Install Required Libraries

First, install the required libraries using pip:

pip install torch torchvision transformers pandas

Step 2: Load the Dataset

For this tutorial, we’ll use the CIFAR-10 dataset, a classic benchmark for image classification tasks. You can download it from the official website.

import os
from torchvision import datasets, transforms

# Define data directories
data_dir = "path/to/cifar-10"
image_dir = os.path.join(data_dir, "cifar-10-batches-py")

# Load CIFAR-10 dataset
transform = transforms.Compose([transforms.ToTensor()])
dataset = datasets.CIFAR10(root=image_dir, train=True, download=True, transform=transform)

Step 3: Prepare the Model

We’ll leverage the Hugging Face Transformers library to create a custom image classification model. Since we’re working with images, we need to adjust the architecture to accommodate this task.

import torch
from transformers import AutoModel, AutoTokenizer

# Load pre-trained models and tokenizer
model_name = "dpt2-large-cosine"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

Step 4: Define the Custom Dataset

We need to create a custom dataset class to handle our CIFAR-10 data. This will involve loading the images and their corresponding labels.

import torch.utils.data as Data
from torchvision import datasets, transforms

class CustomDataset(Data.Dataset):
    def __init__(self, dataset, tokenizer, model):
        self.dataset = dataset
        self.tokenizer = tokenizer
        self.model = model

    def __getitem__(self, index):
        image, label = self.dataset[index]
        encoding = self.tokenizer(image, return_tensors='pt', padding='max_length')
        return {
            'input_ids': encoding['input_ids'],
            'attention_mask': encoding['attention_mask'],
            'labels': torch.tensor(label)
        }

    def __len__(self):
        return len(self.dataset)

Step 5: Train the Model

Now that we have our custom dataset and model, it’s time to train the model. We’ll use a simple training loop to get started.

import torch.nn as nn
from transformers import AdamW, get_linear_schedule_with_warmup

# Define device (GPU or CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Set hyperparameters
batch_size = 32
epochs = 10
learning_rate = 1e-5

# Move model to device
model.to(device)

# Create custom dataset instance
dataset = CustomDataset(dataset, tokenizer, model)
train_dataset = dataset[:batch_size]

# Define optimizer and scheduler
optimizer = AdamW(model.parameters(), lr=learning_rate)
scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=len(train_dataset) * epochs)

# Train the model
for epoch in range(epochs):
    for i in range(len(train_dataset) // batch_size):
        # Zero gradients
        optimizer.zero_grad()

        # Forward pass
        outputs = model(**train_dataset[i * batch_size:(i + 1) * batch_size], labels=train_dataset[i * batch_size:(i + 1) * batch_size]['labels'])

        # Compute loss
        loss = nn.CrossEntropyLoss()(outputs['input_ids'], train_dataset[i * batch_size:(i + 1) * batch_size]['labels'])

        # Backward pass
        loss.backward()

        # Update model parameters
        optimizer.step()

        # Update scheduler
        scheduler.step()

Conclusion

Building a custom image classification model with Hugging Face Transformers on your laptop is now possible. We’ve covered the essential steps, including loading the dataset, preparing the model, defining the custom dataset, and training the model. Remember to always follow best practices when working with deep learning models.

Call to Action

What are some potential applications of this technique in real-world scenarios? Share your thoughts in the comments below!

Build Image Classifier w/ Hugging Face