Waste recycling is very important for economy and climate balance of the world. For this reason, intelligent classifying recyclable garbage is an important goal for humanity and Deep Learning models can be used for this purpose. In this paper, a deep learning framework with different architectures, such as Densenet, Inception- Resnet-V2, MobileNet, and Xception, is tested on Trashnet dataset to provide the most efficient approach. Meanwhile, Adam is selected for optimizing neural network models. Experimental results validate that Deep learning models with the Adam optimizer could provide better a test accuracy rate compared to the Adadelta optimizer. With comparison of quantitative results obtained by those architectures in the deep learning frame- work, we can find that the DenseNet using fine-tuning can get the best result (a test accuracy rate of 95%) and the Inception-ResNet-V2 using fine-tuning is the second best (a test accuracy of 94%).