{"id":29061684,"url":"https://github.com/janaghoniem/fruits-recognition-using-deep-learning-with-data-augmentation","last_synced_at":"2026-04-14T06:33:53.013Z","repository":{"id":297046503,"uuid":"995461115","full_name":"janaghoniem/Fruits-Recognition-Using-Deep-Learning-with-Data-Augmentation","owner":"janaghoniem","description":"A deep learning project for classifying 130+ fruits using EfficientNet, ResNet, and MobileNet with custom augmentations and SE blocks. 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Built on the [Fruits-360 dataset](https://www.kaggle.com/datasets/moltean/fruits), this project focuses on improving robustness with background replacement, noise, blur, and attention mechanisms.\n\n## Overview\n\nTrained and evaluated the following models:\n\n- **EfficientNetB0**\n- **ResNet50**\n- **MobileNetV2**\n\n## Dataset\n\n- **Fruits-360**: 130+ fruit classes with uniform backgrounds\n- All images resized to **100x100**\n- Split into training, validation, and test sets\n\n## Key Features\n\n- Transfer Learning using pretrained CNNs\n- Background replacement with random textures to simulate real-world conditions\n- Extra augmentations: blur, noise, flips, zoom\n- SE block to enhance channel-wise attention\n- Early stopping and model checkpointing\n- Detailed evaluation: classification reports, confusion matrices\n\n## Training Process\n![Image](https://github.com/user-attachments/assets/4ccc9cb3-c13c-4d97-8c9c-b5386218e590)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjanaghoniem%2Ffruits-recognition-using-deep-learning-with-data-augmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjanaghoniem%2Ffruits-recognition-using-deep-learning-with-data-augmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjanaghoniem%2Ffruits-recognition-using-deep-learning-with-data-augmentation/lists"}