Fashion Item Classifier
A Deep Learning project built with TensorFlow and Keras to classify fashion items from the Fashion-MNIST dataset. This project demonstrates the full machine learning workflow: from model training to real-world testing and advanced image preprocessing.
The Challenge
During testing with real-world photos, the model initially struggled to distinguish between complex shapes (like sneakers and bags) due to the limitations of Dense Layers in handling spatial variance. Through iterative debugging and advanced preprocessing, I successfully improved the model's prediction accuracy for custom inputs.
️ Tech Stack
Framework: TensorFlow / Keras
Language: Python
Libraries: NumPy (Data manipulation), Matplotlib (Visualization), PIL/Pillow (Image Processing)
Environment: Google Colab
Model Architecture
The model uses a Sequential Neural Network approach:
Input Layer: Flatten layer to convert 28x28 images into a 1D array.
Hidden Layers: Multiple Dense layers with ReLU activation for feature learning.
Output Layer: Dense layer with Softmax activation to classify 10 distinct fashion categories.
Key Features & Preprocessing
To bridge the gap between "perfect" dataset images and "noisy" real-world photos, I implemented a robust preprocessing pipeline:
Grayscale Conversion: Ensuring compatibility with the single-channel training data.
Thresholding & Contrast: Enhancing object edges to prevent "Bag/T-shirt" misclassification.
Strategic Resizing: Using thumbnail and padding techniques to center-align objects within a 28x28 frame.
Inversion Logic: Automatically adjusting background-to-foreground contrast for optimal detection.
Results
Test Accuracy: Achieved ~88% accuracy on the Fashion-MNIST test set.
Real-World Testing: Successfully classified custom images of T-shirts and Footwear after optimizing the preprocessing pipeline.
Repository Structure
Fashion_Classifier.ipynb: The main notebook containing training and prediction code.
model_weights/: Saved weights for the trained model.
examples/: Sample images used for testing "The Bug" vs "The Fix" scenarios.
See full code in https://lnkd.in/d_NDY5Wf
Learnings
This project was a deep dive into Spatial Invariance and why Convolutional Neural Networks (CNNs) are often preferred over standard Dense Networks for image tasks. It taught me that in AI, data preparation is just as critical as the model architecture itself.