{"id":29022888,"url":"https://github.com/olajuwondele/fruit_image_classification","last_synced_at":"2026-04-10T23:09:03.504Z","repository":{"id":300735280,"uuid":"1006971948","full_name":"OlajuwonDele/fruit_image_classification","owner":"OlajuwonDele","description":"A complete image classification pipeline using TensorFlow/Keras with CNN, MLP, LSTM, Autoencoder, and Vision Transformer models. 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The project demonstrates a complete pipeline from dataset collection to model evaluation.\n\n## Key Features\nDataset Collection: Uses DuckDuckGo Search API to scrape fruit images (grapes, grapefruit, apple, banana, mango, orange)\n\n## Data Preprocessing:\n\nDownloads and verifies image integrity\n\nImplements data augmentation (rotation, zoom, flipping)\n\nSplits data into training/validation sets\n\n## Model Architectures:\n\nCNN (Convolutional Neural Network)\n\nMLP (Multi-Layer Perceptron)\n\nLSTM (Long Short-Term Memory)\n\nAutoencoder Classifier\n\nVision Transformer (ViT)\n\nEvaluation: Comprehensive metrics including accuracy and confusion matrices\n\n## Performance\nThe models achieved the following validation accuracies:\n\nCNN: 70.36%\n\nAutoencoder Classifier: 68.70%\n\nVision Transformer: 65.10%\n\nMLP: 39.06%\n\nLSTM: 29.64%\n\nThe CNN model performed best, highlighting the strength of convolutional architectures for image classification. Although the Vision Transformer (ViT) showed promise, its performance was limited by the relatively small dataset size, which is a known challenge for transformer-based models that typically require large amounts of data to generalize effectively. With more data, ViT models are expected to perform significantly better.\n\n## Technical Details\nFramework: TensorFlow/Keras\n\nImage Size: 150x150 pixels\n\nBatch Size: 32\n\nEpochs: 15\n\nData Augmentation: Rotation, zoom, horizontal flip\n\nValidation Split: 20%\n\nThe notebook provides a solid foundation for image classification tasks and showcases how different architectures perform on the same dataset. The CNN model would be recommended for production use given its superior performance for small datasets, ViT for larger datasets.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Folajuwondele%2Ffruit_image_classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Folajuwondele%2Ffruit_image_classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Folajuwondele%2Ffruit_image_classification/lists"}