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🖼️ ImageClassify-CIFAR10 – v1.0 Release
📅 Release Date
April 20, 2025
🧠 Overview
This is the v1.0 release of ImageClassify-CIFAR10 — a deep learning-based image classification project that uses the CIFAR-10 dataset to accurately identify and categorize images across 10 distinct object classes.
✨ Key Features
🔍 CIFAR-10 Dataset Support
Trained on the CIFAR-10 dataset containing 60,000 32x32 color images across 10 categories:
airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck.
🧠 Deep Learning Model
- Built using Convolutional Neural Networks (CNNs).
- Trained and evaluated using TensorFlow/Keras.
- Achieves high classification accuracy on test data.
📈 Model Performance
- Detailed performance metrics including accuracy, loss, and confusion matrix.
- Real-time training graphs and validation curves.
🖼️ Image Prediction Interface
- Load a custom image and receive predicted label with confidence score.
- Preprocessing pipeline included for resizing and normalization.
⚙️ Tech Stack
- Language: Python
- Frameworks: TensorFlow / Keras
- Libraries:
numpy,matplotlib,sklearn,PIL - Dataset: CIFAR-10 (via
keras.datasets)
🐛 Known Limitations
- Image input must be resized to 32x32 for predictions.
- Model accuracy may vary based on training epochs and batch size.
- Not optimized for deployment (v1.0 focuses on training and evaluation only).
🚀 What's Next (Planned for v1.1)
- Web-based interface for real-time image upload and prediction.
- Transfer learning with pre-trained models (e.g., ResNet, MobileNet).
- Model export as TensorFlow Lite for mobile inference.
- Support for CIFAR-100 and custom datasets.
🙌 Contributors
- Shashwat – Model design, training, evaluation, and visualization