🌟 CNN Image Classification 🌟

Welcome to the CNN Image Classification project! This repository showcases a Convolutional Neural Network (CNN) model built using TensorFlow to classify images into different categories, such as 🌻 sunflowers, 🌹 roses, and 🦋 dandelions.

🚀 Project Overview

• Task: Train a CNN model to classify images into different categories.
• Dataset: Custom dataset crawled from the web (e.g., Baidu Images) with at least three categories and 400+ images per category.
• Model: A CNN architecture built using TensorFlow and Keras for image classification.

🛠️ Steps:

1. 🔍 Data Collection: Crawled images from Baidu Images for multiple categories.
2. 🧼 Data Preprocessing: Split images into training, validation, and testing sets.
3. 🏗️ Model Architecture: Designed a CNN model with convolutional, pooling, and fully connected layers.
4. 🧑‍💻 Training: Trained the model on the dataset and saved it for future use.
5. 📈 Evaluation: Evaluated the model accuracy on the test set.

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📂 Folder Structure

• 📁 SplitData/: Contains the split data for training, validation, and testing.
• 📝 cnn_test.py: Script to test the model accuracy using the test dataset.
• 📝 cnn_train.py: Script to train the CNN model.
• 📝 data_split.py: Script to split the data into training, validation, and testing sets.
• 📝 get_data.py: Script to retrieve or preprocess the dataset.
• 📊 results_cnn.png: A plot visualizing the training and testing results.

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🛠️ Setup and Installation

Prerequisites:

• 🐍 Python (version 3.8)
• 🧠 TensorFlow (version 2.x)
• Other dependencies such as matplotlib, scikit-learn, opencv-python

Installation:

1. Clone this repository:

   git clone https://github.com/yourusername/CNN_image_classification.git
   cd CNN_image_classification

2. Set up a Python virtual environment:

   conda create -n py38 python=3.8
   conda activate py38

3. Install the required dependencies:

   pip install tensorflow-cpu scikit-learn matplotlib seaborn pandas openpyxl opencv-python

4. (Optional) Install PyTorch for additional analysis:

   pip install torch torchvision torchaudio

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💻 Usage

1. 📂 Data Splitting:

   Run the data_split.py script to split your dataset into training, validation, and testing sets:

   python data_split.py

2. 🏋️‍♀️ Model Training:

   Run the cnn_train.py script to start the model training:

   python cnn_train.py

3. 🧪 Model Testing:

   After training, test the model using cnn_test.py:

   python cnn_test.py

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📊 Results

The results from the training process are saved and visualized in results_cnn.png. You can evaluate model performance and accuracy through this visual representation.

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🌐 References

• 🔗 Baidu Images for Crawling
• 📚 TensorFlow Documentation

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🌸 Icons and Indicators

• 🌻 Sunflowers: One of the image categories.
• 🌹 Roses: Another image category.
• 🦋 Dandelions: A third image category.
• 📊 Model Results: A visual indicator of training performance.
• 🧑‍💻 Model Training: Steps for building and training the model.
• 🧪 Model Testing: Steps for evaluating model performance.

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Feel free to explore the repository and enjoy the journey of building your own image classifier! 🚀

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I hope this version adds more visual fun and interest. Let me know if you'd like further tweaks! 😊