How to choose the number of units for the Dense layer in the Convoluted neural network for a Image classification problem?

The number of units in a Dense layer of a convolutional neural network (CNN) for an image classification problem can be chosen based on several factors, including:

1.The complexity of the image classification task: If the task is simple and the images are small, you may only need a few units in the Dense layer. However, if the task is complex and the images are large, you may need more units to capture the necessary information.
2. The size of the input layer: The number of units in the Dense layer should be based on the size of the input layer, which is determined by the size of the images and the number of filters in the convolutional layers.
3. The amount of available data: If you have a large amount of data, you can afford to have more units in the Dense layer, as this will allow the model to learn more detailed patterns in the data.
4. The amount of computational resources available: If you have limited computational resources, you may need to use fewer units in the Dense layer to prevent the model from becoming too large.

It's generally a good idea to start with a smaller number of units and gradually increase them until you see improvement in the model's performance. You can also try using different architectures, such as adding more layers or using different types of layers, to see if they improve the model's performance.