I don't think it makes sense these days to implement a CNN architecture from scratch for a standard problem (e.g., classification), except as a learning exercise. A common set of classification networks that I use as a go-to are the EfficientNet architectures. Usually I use the timm library (for PyTorch), and instantiating the model is just 1 line of code (see its docs). You can either load it in pretrained (from ImageNet) or randomly initialized, and further fine-tune yourself. EfficientNet has versions 0-7 that give increasing performance at the cost of computation/size. If you're in TensorFlow-land I'm sure there's something analogous. Both TF and PT have model zoos in official packages too. Like torchvision.models or whatever.