I am trying to build a classification algorithm having 28 classes. These classes consists of Logo of companies like adidas , Nike etc. I have very low dataset below than 100 images and greater than 70 images. I have trained CNN model but not got decent results . Accuracy is not good. I want to switch on pretrained model. I don't know which pretrained model should I use either VGG , ResNet etc. because I saw on Internet that you may use Pretrained model on similar dataset but I have logo of companies , I don't know that any pre trained model is trained on such similar type of objects or not. How should I choose pre trained model that will performs well on my dataset.
There's no general answer. In this field, you'll just have to try multiple approaches and see how well they work. We usually won't know what will work best until we try it.
I can suggest three candidate approaches:
Get more data.
Use a pre-trained model and fine-tune it. You can use any state-of-the-art model, pick a version that was pre-trained on ImageNet, and then fine-tune it (either fixing the first n-1 layers and training only the last layer; or training all layers, but with a smaller step size).
Use few-shot learning. This will probably require more sophisticated methods and more study.
Aggressive data augmentation might be useful.