Collect and label the data: The first step is to collect a large dataset of images and label them according to the category they belong to. The dataset should be representative of the real-world scenario you want to apply the model to.

Preprocess the data: Before training the model, it is important to preprocess the data. This includes resizing the images, normalizing the pixel values, and possibly augmenting the data to increase the size of the dataset.

Choose a model architecture: There are many pre-trained models available for image classification, such as VGG, ResNet, and InceptionNet. You can also use a model that has been trained on a similar dataset, or you can create your own model architecture.

Train the model: Once the data is preprocessed and the model architecture is chosen, the model can be trained using the labeled data. This is typically done using a deep learning framework such as TensorFlow or PyTorch.

Evaluate the model: After the model is trained, it should be evaluated using a separate test dataset to determine its accuracy and performance.

Fine-tune the model: If the performance of the model is not satisfactory, you may want to fine-tune the model by adjusting the hyperparameters, adding more data, or changing the model architecture.

Deploy the model: Once the model has been fine-tuned and its performance is satisfactory, it can be deployed in a production environment.

It's important to note that image classification is a complex task and it can be time-consuming and computationally intensive, so it's recommended to have a good understanding of machine learning and image processing techniques and have access to computational resources.