Project3: Image Classification
A deep learning model that classifies images into categories.
Project Overview
This project involves building a deep learning model using convolutional neural networks (CNNs) to classify images into predefined categories. The model is trained on a large dataset of images and can be used for various applications such as object detection, image recognition, and more.
Technical Details
The model is built using TensorFlow and Keras, utilizing a CNN architecture with multiple layers to extract features from images. The training process involves data augmentation to improve the model's robustness and accuracy.
import tensorflow as tf from tensorflow import keras # Define the model architecture model = keras.Sequential([ keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)), keras.layers.MaxPooling2D((2, 2)), keras.layers.Flatten(), keras.layers.Dense(128, activation='relu'), keras.layers.Dropout(0.2), keras.layers.Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
Results and Future Work
The model achieves a high accuracy rate on the test dataset. Future work includes expanding the dataset to include more categories and improving the model's performance using transfer learning and other advanced techniques.