{"id":27979302,"url":"https://github.com/srepasup/cat-and-dog-image-classifier","last_synced_at":"2026-05-05T23:31:45.179Z","repository":{"id":291695642,"uuid":"978471360","full_name":"srepasup/Cat-and-Dog-Image-Classifier","owner":"srepasup","description":"A convolutional neural network (CNN) built with TensorFlow and Keras to classify images of cats and dogs with over 63% accuracy. 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It uses a dataset of 3,000+ labeled images and achieves over 70% accuracy.\n\n✅ Trained on 2,000 images  \n✅ Validated on 1,000 images  \n✅ Tested on 50 unlabeled images  \n\n## 🎯 Objective\nTrain a CNN model that can classify cat and dog images with over 63% accuracy.\n\n## 📊 Dataset\nThe dataset is provided by freeCodeCamp and includes:\n- 2,000 training images\n- 1,000 validation images\n- 50 test images (unlabeled)\n\nThe folder structure:\ncats_and_dogs/\n├── train/\n│ ├── cats/\n│ └── dogs/\n├── validation/\n│ ├── cats/\n│ └── dogs/\n└── test/\n└── [unlabeled images]\n\n\n## 🧪 Model Summary\n- Preprocessing with `ImageDataGenerator` (rescaling + augmentation)\n- CNN with Conv2D, MaxPooling2D, and Dense layers\n- Trained for 15 epochs with a batch size of 128\n\n## 📈 Results\n- Achieved over **63% accuracy**\n- Successfully passed the automated test\n- Visualized predictions on test data\n\n## 🛠 Tools Used\n- Python 3.x\n- TensorFlow 2.x\n- Keras\n- Google Colab\n- Matplotlib \u0026 NumPy\n\n## 🚀 How to Run\n1. Open the `.ipynb` notebook in Google Colab.\n2. Run all cells in order.\n3. Make sure the dataset downloads and unzips correctly.\n4. After training, evaluate and visualize predictions in Cells 10 \u0026 11.\n\n---\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsrepasup%2Fcat-and-dog-image-classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsrepasup%2Fcat-and-dog-image-classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsrepasup%2Fcat-and-dog-image-classifier/lists"}