cat4eve/animalimageclassification

**Animal Classification with MobileNet.**

This project demonstrates the process of training a deep learning model to classify images of animals using the MobileNet architecture. The model is trained on a dataset of animal images and is capable of predicting the class of a given animal image.

**Dataset Architecture**

Dataset

animals/
    train/
        class1/
            image1.jpg
            image2.jpg
            ...
        class2/
            image1.jpg
            image2.jpg
            ...
        ...
    validation/
        class1/
            image1.jpg
            image2.jpg
            ...
        class2/
            image1.jpg
            image2.jpg
            ...
        ...

**Model Architecture**

The MobileNet model, pre-trained on the ImageNet dataset, is used as the base model. Custom layers are added on top of the MobileNet architecture to adapt it for animal classification. The model consists of a global average pooling layer, followed by a dense layer with 1024 units and ReLU activation. The final dense layer with softmax activation is added to predict the class probabilities of the input image.

**Training**

During training, data augmentation techniques such as rescaling, shearing, zooming, and horizontal flipping are applied to the training images. The images are also normalized by dividing the pixel values by 255. The Adam optimizer is used with categorical cross-entropy loss as the training objective. The model is trained for a specified number of epochs with a given batch size.

**Evaluation**

The model's performance is evaluated using accuracy as the evaluation metric. The validation dataset is used for evaluation, and the accuracy is reported after each epoch.

**Saving the Model**

After training, the trained model is saved as "animal_classification_model.h5". Additionally, the list of class names is saved as "class_names.pkl" using the pickle library for later reference.