Update README.md

README.md

@@ -1,3 +1,69 @@
 ---
 license: apache-2.0
+pipeline_tag: image-classification
+tags:
+- aesthetic
 ---
+
+# THE INPUT IMAGE MUST HAVE `RGB` CHANNELS. IT WILL NOT WORK WITH `RGBA` CHANNELS!
+
+## Usage
+```python
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.transforms as transforms
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+class CNN(nn.Module):
+    def __init__(self, hidden_size=512):
+        super(CNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
+        self.fc1 = nn.Linear(32 * 192 * 192, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, 2)
+
+    def forward(self, x):
+        x = torch.relu(self.conv1(x))
+        x = torch.max_pool2d(x, kernel_size=2, stride=2)
+        x = torch.relu(self.conv2(x))
+        x = torch.max_pool2d(x, kernel_size=2, stride=2)
+        x = x.view(-1, 32 * 192 * 192)
+        x = torch.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+model = CNN().to(device).half()
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=2.5e-5)
+
+transform = transforms.Compose([
+    transforms.Resize((768, 768)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+])
+
+def infer(model, image_path):
+    model.eval()
+    image = Image.open(image_path)
+    image = transform(image).unsqueeze(0).to(device).half()
+    with torch.no_grad():
+        output = model(image)
+    predicted_class = torch.argmax(output).item()
+    return predicted_class
+
+checkpoint = torch.load('half_precision_model_checkpoint.pth')
+model.load_state_dict(checkpoint['model_state_dict'])
+optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+epoch = checkpoint['epoch']
+loss = checkpoint['loss']
+
+image_path = 'good.jpg'
+predicted_class = infer(model, image_path)
+if int(predicted_class) == 0:
+    print('Predicted class: Bad Image')
+elif int(predicted_class) == 1:
+    print('Predicted class: Good Image')
+```