To design a Convolutional Neural Network (CNN) architecture based model to classify cat and dog photos, you can follow these steps:

1. Collect and preprocess the data. You can use a dataset such as the Cats and Dogs Dataset which contains 25,000 images of cats and dogs. You can preprocess the data by resizing the images to a fixed size, normalizing the pixel values, and splitting the data into training and validation sets.

2. Design the CNN architecture. You can use a simple CNN architecture consisting of convolutional layers followed by max pooling layers. You can add more convolutional layers to increase the depth of the network. You can also add dropout layers to prevent overfitting.

3. Train the CNN model. You can use an optimizer such as Adam to minimize the loss function during training. You can also use techniques such as data augmentation to increase the size of the training set and prevent overfitting.

4. Evaluate the CNN model. You can evaluate the performance of the model on the validation set using metrics such as accuracy, precision, recall, and F1 score.

5. Fine-tune the CNN model. You can fine-tune the hyperparameters of the model such as learning rate, batch size, number of epochs, etc., to improve its performance.

Some pitfalls that you should avoid while designing a CNN architecture are:

1. Overfitting: Overfitting occurs when a model is too complex and learns to fit the training data too well. To prevent overfitting, you can use techniques such as dropout layers, data augmentation, early stopping, etc.

2. Underfitting: Underfitting occurs when a model is too simple and cannot capture the complexity of the data. To prevent underfitting, you can increase the depth of the network or add more convolutional layers.

3. Vanishing gradients: Vanishing gradients occur when gradients become too small during backpropagation and prevent the network from learning. To prevent vanishing gradients, you can use activation functions such as ReLU or LeakyReLU.

I hope this helps! Let me know if you have any other questions.
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