Calibration Datasets

For the quantization process, the OpenVINO POT requires a calibration dataset. The goal of the project is to use synthetic images generated by DiStyleGAN as the calibration dataset. However, we also conducted the quantization using three other datasets. In particular, we use the following calibration datasets in our experiments:

• Official CIFAR-10 training set
• Synthetic images generated by StyleGAN2-ADA
• Synthetic images generated by DiStyleGAN
• Fractal images generated by using the Datumaro's repository on GitHub (It is important to note that while the synthetic datasets above approximate the CIFAR-10 distribution, thus could be considered representative, the fractal images do not constitute a representative dataset for the deep learning models pre-trained on CIFAR-10.)

We used 5,000 images from each of the aforementioned datasets, 500 images per class of the CIFAR-10 dataset. These subsets can be downloaded from here, or you can generate them following the instructions in the links. We then evaluated the results of the quantization methods on the classification task, for the selected PyTorch models, using the CIFAR-10 test set.