VLG Dataset Image Classification Project

🌟 Overview

🗂️ This repository contains resources and code for classifying images into 50 📚 categories using 🏋️‍♂️ ResNet50. It details steps such as data preprocessing, model fine-tuning, training, and 📈 making predictions, providing a robust framework for image classifications.

---

📁 Repository Structure

• 📝 notebook.ipynb: 📘 Jupyter notebook with complete 🚀 implementation pipeline.
• 💾 best_model.pth: 🔒 Checkpoint for the trained ResNet50 model.
• 📊 predictions.csv: 📑 File containing submission-ready 📝 predictions for the test dataset.
• 📄 README.md: 📜 File explaining 📚 project details and instructions.

---

⚙️ Prerequisites

🔧 Install these 🛠️ tools to set up the environment:

• 🐍 Python
• 🔥 PyTorch
• 📸 torchvision
• 📐 scikit-learn
• 🎨 matplotlib
• 📊 pandas
• 📓 Jupyter Notebook

---

🗂️ Datasets

📂 The dataset is divided into 🏋️‍♂️ training, 📊 validation, and 📁 test sets. It facilitates essential tasks:

• 📥 Loading and preprocessing 🖼️ data.
• 🔀 Splitting data into training and validation subsets.
• 🛠️ Generating predictions for unseen test data.

---

🔑 Key Steps in Implementation

1️⃣ 📥 Data Loading & Preprocessing

• 🔎 Description: The dataset is read, resized to 224x224 pixels, normalized for ResNet50, and converted into tensor format. Each image undergoes transformation to maintain consistency with ResNet50 input requirements.
• 🧑‍💻 Code Highlights:

  transforms.Compose([
      transforms.Resize((224, 224)),
      transforms.ToTensor(),
      transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
  ])

• Rationale: These transformations ensure pixel intensities are scaled, and the model input format is maintained.

2️⃣ 🛠️ Model Fine-Tuning

• 🔎 Description: The ResNet50 architecture is modified to support 50 🏷️ output categories by adding a custom fully connected layer. A dropout layer is included to reduce overfitting.
• 🧑‍💻 Code Highlights:

  model.fc = nn.Sequential(
      nn.Linear(num_features, 512),
      nn.ReLU(),
      nn.Dropout(p=0.5),
      nn.Linear(512, 50)
  )

• Rationale: Transfer learning leverages pretrained weights, while the fully connected layer adapts the model to our specific classification task.

3️⃣ 🏋️‍♂️ Training & Validation

• 🔎 Description: The model is trained using the Adam optimizer with weight decay for regularization, and a StepLR scheduler dynamically adjusts the learning rate to enhance convergence.
• 🧑‍💻 Code Highlights:

  scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.1)
  torch.save(model.state_dict(), "best_model.pth")

• Rationale: The combination of optimization techniques ensures efficient training and prevents overfitting.

4️⃣ 🔍 Evaluation

• 🔎 Description: The model is evaluated on validation data using metrics such as accuracy and loss, with trends plotted to monitor training effectiveness.
• Key Metrics: Validation accuracy of ~98.01% highlights the model’s strong generalization.

5️⃣ 📤 Prediction & Submission

• 🔎 Description: Predictions are generated for the test dataset, and results are formatted into a CSV file for submission.
• 🧑‍💻 Code Highlights:

  test_preds = []
  for images in test_loader:
      outputs = model(images.to(device))
      _, preds = torch.max(outputs, 1)
      test_preds.extend(preds.cpu().numpy())

• Rationale: This process ensures compatibility with competition requirements.

---

🏆 Results

• 🎯 Training Accuracy: Achieved an impressive 📈 ~95.63%.
• 🎯 Validation Accuracy: Reached 📊 ~98.01%, demonstrating robust model generalization.

🌟 Vital Components

1️⃣ 🏋️‍♂️ Pretrained ResNet50: Utilizes transfer learning for high 🎯 accuracy. 2️⃣ 🧠 Custom Fully Connected Layer: Adapts ResNet50 for the 50-category 🏷️ classification task. 3️⃣ 🎚️ Learning Rate Scheduler: Dynamically adjusts learning rates for efficient training. 4️⃣ ⛔ Dropout Layers: Reduces 📉 overfitting, ensuring better generalization on unseen datasets. 5️⃣ 📥 Data Preprocessing: Rescales and normalizes images to align with model input requirements. 6️⃣ 🛠️ Custom Training Loop: Integrates evaluation and scheduler updates within each epoch for streamlined performance monitoring.

---

💬 Comments & Documentation

💡 The code is extensively commented to provide clear explanations for:

• 🔧 Data preprocessing steps.
• 🛠️ Model 🎛️ architecture modifications.
• 🔄 Training and validation loops.
• 📊 Test data prediction and result generation.

---

🌟 Conclusion

This project showcases a robust approach to image classification using a ResNet50 backbone, custom fully connected layer, and a learning rate scheduler. It provides a solid foundation for further exploration and fine-tuning for optimal performance in image classification tasks.