One of the classical problems in the field of computer vision and machine learning and subsequently deep learning is image classification. While Deep Learning solves the much difficult hurdles like feature extraction and presents us with better optimizations like gradient descent and Adam optimizer, most deep learning models still need a lot of raw computational power to train models on local Graphical Processing Units (GPUs) or Tensor Processing Units (TPUs) in the cloud. All of this computational power is not readily available in all environments and systems and hence the concept of pre-trained models can help to reduce training time by a huge margin. Initial models get trained on large array of GPUs and do feature extraction. The classification part is for the end-user to customize in accordance to the problem at hand and can be completed in very less time. We tackled the multi-class classification botanical problem of identifying flowers of 5 types, namely, Sunflower, Rose, Dandelion, Daisy, and Tulip. The feature extraction part is done with the model (Google’s Inception-v3) and fully connected softmax layers were trained on local machine on a Nvidia GeForce GTX 950 (with CUDA activated) within 30 minutes time and total steps/epochs were 4000 only. The total number of training images is 3,500 (approx.). The finished model produced results with final test accuracy as 91.9% on new images (N=664).