<p>If you're looking for the best CNN model for image classification, take a look at EfficientNet architecture (<a href="https://github.com/lukemelas/EfficientNet-PyTorch" rel="nofollow noreferrer">Pytorch implementation</a>, <a href="https://arxiv.org/abs/1905.11946" rel="nofollow noreferrer">Paper</a>). IIRC, Googlenet is kinda old.</p>
<p>If your model requires some specific shape of the input image, you can just resize them (for example, you can use <a href="https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize" rel="nofollow noreferrer">torchvision</a> or <a href="https://www.tutorialkart.com/opencv/python/opencv-python-resize-image/" rel="nofollow noreferrer">OpenCV</a>)</p>
Will it be okay if I use my image datasets with 50x 50 dimensions into googLeNet CNN architecture which is recommending for 224 x 224?
<p>I am new to DL and am trying to train my first CNN models with <strong>googLeNet</strong> architecture. I've prepared my custom image data dimensions with 50x50 but the architecture is recommending to use 224x224. Will it be okay to use the architecture? I don't want to remake my datasets to change the size of the images. So, if there are some other architectures that I can look into it, please kindly recommend them for me.</p>