---
license: mit
---

## Demo
https://huggingface.co/spaces/jerilseb/quickdraw-small

## Usage

```python
import torch
from torch import nn
import torchvision.transforms as transforms
import torch.nn.functional as F
from pathlib import Path

LABELS = Path("classes.txt").read_text().splitlines()
num_classes = len(LABELS)

model = nn.Sequential(
    nn.Conv2d(1, 64, 3, padding="same"),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(64, 128, 3, padding="same"),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(128, 256, 3, padding="same"),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Flatten(),
    nn.Linear(2304, 512),
    nn.ReLU(),
    nn.Linear(512, num_classes),
)

state_dict = torch.load("model.pth", map_location="cpu")
model.load_state_dict(state_dict)
model.eval()

transform = transforms.Compose(
    [
        transforms.Resize((28, 28)),
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,)),
    ]
)

def predict(image):
    image = image['composite']
    tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        out = model(tensor)

    probabilities = F.softmax(out[0], dim=0)
    values, indices = torch.topk(probabilities, 5)
    print(values, indices)
```