Fashion Style Classifier

A comprehensive workflow for classifying fashion images into style categories using both deep learning (CNN) and classical machine learning (KNN). This project demonstrates data collection, preprocessing, model training, evaluation, and reporting for fashion style recognition.

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Experimental Setup & Key Findings

Experimental Setup

• Data Source: Images scraped from Reddit (r/streetwear, r/fashion, r/femalefashionadvice), manually labeled using Label Studio.
• Preprocessing: Cleaning missing/corrupt files, class balance visualization, stratified train/test split.
• Models:
  • CNN: EfficientNet-B0 (PyTorch, timm), transfer learning, data augmentation, 20 epochs, Adam optimizer.
  • KNN: Deep features from MobileNetV2 (Keras), PCA for dimensionality reduction, KNeighborsClassifier (scikit-learn), GridSearchCV for hyperparameter tuning.
• Evaluation: Accuracy, F1-score, confusion matrix, classification report.
• Reproducibility: All code and steps are in Jupyter notebooks with cell-by-cell documentation.

Key Findings

• Both CNN and KNN achieved ~41% accuracy, with best F1-scores for Formal and Streetwear classes (~0.55).
• Sporty class was poorly classified (F1 ~0.00) due to severe class imbalance.
• Data augmentation and transfer learning improved CNN performance, but minority classes remain challenging.
• KNN with deep features is a strong baseline but less robust than CNN for complex patterns.

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Challenges & Literature Context

Challenges

• Class Imbalance: Sporty and Vintage classes had far fewer samples, leading to poor generalization and low F1-scores.
• Noisy Real-World Data: Images scraped from Reddit are diverse, with varying quality, backgrounds, and styles, making feature extraction and classification difficult.
• Limited Labeled Data: Manual annotation is time-consuming, restricting dataset size and diversity.
• Model Selection: Balancing between deep learning (CNN) and classical ML (KNN) for a non-standard dataset.

Literature Context

• Existing Solutions:
  • Deep learning models (EfficientNet, ResNet, Vision Transformers) are state-of-the-art for image classification (Tan & Le, 2019).
  • Transfer learning and data augmentation are widely used to improve performance on small datasets.
  • Class imbalance is a common issue; solutions include oversampling, class weighting, and focal loss (Lin et al., 2017).
  • KNN with deep features is a simple, interpretable baseline but less effective for complex, imbalanced data.

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Repository Navigation & Rerunning Experiments

Repo Structure

• data/images/ — Fashion images
• data/labels/labels_file.csv — CSV with image filenames and style labels
• fashion_image_classification_CNN.ipynb — CNN workflow (EfficientNet-B0)
• fashion_image_classification_knn.ipynb — KNN workflow (MobileNetV2 features)
• requirements.txt — Python dependencies

How to Rerun Experiments

1. Clone the repository:

   git clone https://github.com/AadrianLeo/Fashion-Style-Classifier.git
   cd Fashion-Style-Classifier

2. Install dependencies:
   • Python 3.8+
   • Install required packages:

     pip install -r requirements.txt

   • For KNN feature extraction:

     pip install tensorflow keras

   • For CNN:

     pip install torch torchvision timm

3. Prepare the data:
   • Place the labeled CSV and image files in the expected directories (data/images/, data/labels/labels_file.csv).
4. Run notebooks:
   • Open fashion_image_classification_CNN.ipynb and fashion_image_classification_knn.ipynb in Jupyter or VS Code.
   • Run all cells sequentially; each cell is documented for clarity.
   • Results (metrics, confusion matrices, sample predictions) are displayed in the output cells.

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Table of Contents

• Project Overview
• Dataset
• Workflow
• Models
  • CNN (EfficientNet-B0)
  • KNN
• Results
• Future Work
• How to Run
• References

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Project Overview

This project aims to classify fashion images into five style categories: casual, formal, streetwear, sporty, and vintage. Images were scraped from Reddit and manually labeled using Label Studio. The workflow includes feature extraction, data preprocessing, model training (CNN & KNN), evaluation, and visualization.

Dataset

• Source: Reddit (subreddits: r/streetwear, r/fashion, r/femalefashionadvice)
• Annotation: Manual labeling via Label Studio
• Structure:
  • data/images/ — Fashion images
  • data/labels/labels_file.csv — CSV with image filenames and style labels

Workflow

1. Data Collection: Scrape images from Reddit using PRAW and requests.
2. Annotation: Label images in Label Studio; export CSV.
3. Preprocessing:
   • Clean missing labels
   • Visualize samples and class distribution
   • Stratified train/test split
4. Feature Extraction:
   • CNN: Use raw images
   • KNN: Extract features using MobileNetV2
5. Model Training:
   • CNN: EfficientNet-B0 (PyTorch, timm)
   • KNN: Scikit-learn pipeline (scaling, PCA, KNN)
6. Evaluation:
   • Accuracy, F1-score, confusion matrix
   • Visualizations and result summaries

Models

CNN (EfficientNet-B0)

• Framework: PyTorch, timm
• Architecture: EfficientNet-B0, pretrained on ImageNet, fine-tuned for 5 classes
• Augmentation: Resize, random flip, rotation, color jitter
• Training: 20 epochs, Adam optimizer, cross-entropy loss
• Evaluation: Classification report, confusion matrix

KNN

• Feature Extraction: MobileNetV2 (Keras, TensorFlow)
• Dimensionality Reduction: PCA (retain 95% variance)
• Classifier: KNeighborsClassifier (scikit-learn)
• Hyperparameter Tuning: GridSearchCV (n_neighbors, weights, metric)
• Evaluation: Accuracy, F1-score, confusion matrix

Results

• Overall Accuracy: ~41% (both CNN & KNN)
• Best Classified Styles: Formal, Streetwear (F1 ~0.55)
• Worst Classified Style: Sporty (F1 ~0.00, due to class imbalance)
• Macro Avg. F1-score: 0.29
• Confusion Matrix: See notebook for visualizations

Future Work

• Explore advanced architectures (EfficientNetV2, Vision Transformers)
• Apply more aggressive data augmentation
• Address class imbalance with oversampling or synthetic data
• Fine-tune hyperparameters and try alternative feature extractors
• Integrate explainability tools (e.g., Grad-CAM)
• Deploy best model as a web/mobile app

How to Run

1. Clone the repository:

   git clone https://github.com/AadrianLeo/Fashion-Style-Classifier.git
   cd Fashion-Style-Classifier

2. Install dependencies:
   • Python 3.8+
   • Install required packages:

     pip install -r requirements.txt

   • For KNN feature extraction:

     pip install tensorflow keras

   • For CNN:

     pip install torch torchvision timm

3. Run notebooks:
   • Open fashion_image_classification_CNN.ipynb and fashion_image_classification_knn.ipynb in Jupyter or VS Code
   • Follow cell-by-cell instructions

References

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• MobileNetV2: Inverted Residuals and Linear Bottlenecks
• Label Studio
• PyTorch, scikit-learn, TensorFlow

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For questions or contributions, please open an issue or submit a pull request.