Sure, here are the steps on how to design a CNN architecture based model to classify cat and dog photos:

1. **Gather your data.** The first step is to gather a dataset of cat and dog photos. You can find datasets online, or you can collect your own photos. Once you have your dataset, you need to split it into training and testing sets. The training set will be used to train your model, and the testing set will be used to evaluate your model's performance.
2. **Preprocess your data.** Once you have your training and testing sets, you need to preprocess your data. This involves resizing the images to a common size and normalizing the pixel values.
3. **Design your CNN architecture.** There are many different CNN architectures that you can use. A popular architecture is the VGGNet architecture. You can find the VGGNet architecture online.
4. **Compile and train your model.** Once you have designed your CNN architecture, you need to compile and train your model. This involves using a deep learning framework, such as TensorFlow or PyTorch.
5. **Evaluate your model.** Once you have trained your model, you need to evaluate its performance on the testing set. This will give you an idea of how well your model will generalize to new data.

Here are some pitfalls that you should avoid when designing a CNN architecture based model to classify cat and dog photos:

* **Using too many layers.** Too many layers can make your model slow to train and can also lead to overfitting.
* **Using too small of a learning rate.** A too small learning rate can make your model train very slowly.
* **Not using enough data.** Not using enough data can lead to overfitting.
* **Not evaluating your model on a testing set.** Not evaluating your model on a testing set can give you a false sense of how well your model performs.

I hope this helps!
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