This repository includes codes for training and evaluating the Mask Classifier model, which is mostly implemented in Python 3 and PyTorch framework.

You can download Mask Classifier dataset from here and then save it into a folder name data/

In reality, very few people train the entire Convolutional Network from scratch (with random initialization) because it is fairly difficult to have a data set of adequate scale. Alternatively, it is popular to pre-train ConvNet on a very large dataset (e.g. ImageNet, which includes 1.2 million images with 1000 categories) and then use ConvNet either as an initialization or as a fixed feature extractor for the task of interest.

Two major transfer learning scenarios appear as follows:

Instead of random initialization, we initialise the network with a pre-trained network, like the one trained on the imageet 1000 dataset. The remainder of the preparation is as normal.

We freeze the weights for the entire network except for the final fully connected layer. This last fully connected layer is replaced by a new one with random weights and only this layer is trained.

Thanks! ❤️ ❤️ ❤️