Low-Shot Color Classifier

An end-to-end pipeline for low-shot image classification, designed to bridge the gap between manual data collection and deep learning. This project features a complete workflow: mobile data collection via AppSheet, an automated cleaning suite, a custom PyQt6 curation GUI, and a high-performance Convolutional Neural Network (CNN) built with TensorFlow/Keras.

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🚀 Features

• AppSheet Integration: Seamlessly fetch image data and metadata collected from mobile devices using the AppSheet API.
• Automated Pre-cleaning: Scripts to automatically remove duplicate images (via SHA-256 hashing) and filter out low-resolution samples.
• Interactive Curation: A custom-built PyQt6 desktop application to manually review and select high-quality samples for the "low-shot" training set.
• Cost-Sensitive CNN: A specialized TensorFlow model that utilizes a custom loss function to penalize specific class confusions (e.g., Blue vs. Purple) more heavily.
• Advanced Data Augmentation: Custom layers for Saturation Jitter and Random Erasing to improve model robustness with minimal data.

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📁 Project Structure

• GetData.py: Fetches metadata and downloads images from the AppSheet cloud.
• CleanData.py: Automated removal of duplicates and low-res images.
• SelectImages.py: PyQt6 GUI for manual image selection and labeling verification.
• MoveFiles.py: Utility to organize selected images into the final training directory.
• CNN.ipynb: The training pipeline, including data preprocessing (HSV conversion), model architecture, and evaluation.

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🛠️ Installation & Setup

1. Prerequisites

Ensure you have Python 3.10+ installed. It is recommended to use a virtual environment.

pip install tensorflow opencv-python pandas pillow pyqt6 scikit-learn seaborn python-dotenv

2. Environment Variables

Create a .env file in the root directory to store your AppSheet credentials:

APP_ID=your_appsheet_id
ACCESS_KEY=your_access_key
TABLE_NAME=your_table_name

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🔄 Workflow Execution

Follow these steps in order to process your data and train the model:

Step 1: Data Collection

Collect your color samples using your configured AppSheet app. Once ready, run the retrieval script:

python GetData.py

This downloads images to the /images folder and generates metadata.csv.

Step 2: Automated Cleaning

Remove redundant or poor-quality files:

python CleanData.py

• Duplicates: Removed using SHA-256 hash comparison.
• Resolution: Defaults to removing images smaller than 150x150 pixels.

Step 3: Manual Curation (Low-Shot Selection)

Launch the GUI to hand-pick the best representatives for each class:

python SelectImages.py

• Controls: Click images to toggle selection (Green = Keep, Red = Drop).
• Output: Saves your choices to selected_images.csv.

Step 4: Finalize Dataset

Organize the selected files into the folder structure required by the model:

python MoveFiles.py

Step 5: Training & Evaluation

Open CNN.ipynb in Jupyter or Google Colab. The notebook performs:

1. Preprocessing: Converts images to HSV color space to better isolate "Color" features.
2. Training: Executes a 3-block CNN with Global Average Pooling and Dropout.
3. Cost-Sensitive Loss: Uses a COST_MATRIX to specifically penalize confusion between Blue and Purple.

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📊 Model Performance

The current architecture, KHILONA_CNN, achieves high overall accuracy of 88.24% even with very small datasets of 75 samples per class by leveraging HSV color space and strategic augmentation.

Class	Precision	Recall	F1-Score
Blue	0.83	0.91	0.87
Purple	0.82	0.82	0.82
Yellow	1.00	0.92	0.96

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