The project aims to implement image classification using Convolutional Neural Networks (CNN) in Python with the help of TensorFlow. The project utilizes the CIFAR10 dataset, which is a popular benchmark dataset in the field of image classification.

The CNN Sequential model is used as the model architecture, involving the stacking of multiple convolutional layers, pooling layers, and dense layers to achieve high accuracy in image classification.

In addition, various libraries are used, including tensorflow.keras.models, tensorflow.keras.layers, tensorflow.keras.preprocessing.image, numpy, Matplotlib.pyplot, and seaborn. The dataset is preprocessed by normalizing the pixel values and performing data augmentation to increase the robustness of the model. After training, evaluation metrics such as accuracy, precision, recall, and F1-score are calculated to assess the performance of the model.

Finally, the project uses visualization techniques such as confusion matrix and classification report to interpret the results and improve the model's performance.

The results favour CNN over ANN as CNN can detect features independent of their postion via convolution operation which is ground breaking in field of AI and image processing.