This paper explores EfficientNet smaller models for a multiclassification task of corrosion types in above-ground storage tanks through transfer learning and finetuning approaches. Data augmentation was used to increment the data an oil and gas company provided, reaching a dataset of around 5000 images. The images were stored in Google Drive and imported by Colab to obtain the models using TensorFlow and Keras. After the hyperparameters’ tuning a transfer learning model was selected and explored with fine tuning. The EfficientNetB0 model delivered from fine-tuning accomplished 94% performance. This work is the first attempt to deploy an artificial vision automatic tool for being implemented during tank inspection in the industrial sector. In a further development, this model can be coupled with one based on object detection for the remote identification of failures due to external corrosion during tank inspection; improving safety and reliability in the oil and gas industry.