Like SwinOCSR, DECIMER 1.0 and DECIMER.ai are also designed for reading molecule images and output as standard notations. DECIMER.ai is an improved version of the 1.0 version. Their main differences are encoder architecture, if the encoder is frozen, image size in the dataset, and different notation used. In the 1.0 version, the encoder is an EfficientNet-B3 and is likely frozen during training. On the other hand, in DECIMER.ai, the encoder is an Efficient-V2-M . Instead of the image size of 299×299 in the 1.0 version, DECIMER.ai uses 512×512 to provide sufficient information for bigger molecules. Based on their previous study, although DeepSMILES has been proposed for machine learning models to generate more valid identifiers, SMILES lead to the most accurate identifiers, and SELFIES lead to the most valid sequences, and DeepSMILES is between the two . Thus, DECIMER.ai used SMILES instead of SELFIES like in DECIMER 1.0 .