Inspired by @bwasti’s awesome image-to-emoji project, I built a PyTorch model that renders images as a series of 10-pixel by 10-pixel emojis.
I created the dataset by downloading the 14-pixel by 14-pixel png images of emojis from https://unicode.org/emoji/charts/full-emoji-list.html.
For transformation, I just did transforms.GaussianBlur(kernel_size=(3, 3), sigma=(0.1, 0.9)) and transforms.RandomRotation(degrees=5).
For the model, first, I overfitted torchvision.models.resnet18() and then this small model. Both worked fine, but I like the output of the resnet18 more.
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class EmojiNet(nn.Module):
def __init__(self, num_classes):
super(EmojiNet, self).__init__()
self.feature_extractor = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=8, kernel_size=3, stride=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(in_channels=8, out_channels=32, kernel_size=3, stride=1),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.clasifier = nn.Sequential(
nn.Linear(in_features=32, out_features=256),
nn.ReLU(),
nn.Linear(in_features=256, out_features=num_classes),
)
def forward(self,x):
x = self.feature_extractor(x)
x = torch.flatten(x, 1)
x = self.clasifier(x)
return x
Resnet18 output: 
EmojiNet output: 
I need to work on improving its speed (I have a problem making predictions on a batch of inputs), but I’m super happy with the results. I’ll clean the code and release it sometime in the future and update this page :)
