Create codefortraining.py

codefortraining.py

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+import torch
+import torchvision
+from torchvision.models import resnet50
+from torchvision.transforms import transforms
+from torch.utils.data import DataLoader, Dataset
+
+# Define the dataset class
+class RobloxDataset(Dataset):
+    def __init__(self, root_dir, transform=None):
+        self.root_dir = root_dir
+        self.transform = transform
+        
+    def __len__(self):
+        return 200  # Number of images in the dataset, replace with your own to train with your own images.
+    
+    def __getitem__(self, idx):
+        img_path = f'{self.root_dir}/human_{str(idx+1).zfill(2)}.png'
+        image = Image.open(img_path).convert('RGB')
+        
+        if self.transform:
+            image = self.transform(image)
+        
+        return image
+
+data_transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+])
+
+dataset = RobloxDataset('/content/dataset', transform=data_transform)
+
+data_loader = DataLoader(dataset, batch_size=1, shuffle=True)
+
+model = resnet50(pretrained=True)
+model.fc = torch.nn.Linear(in_features=2048, out_features=1)  # Adjust the number of output classes if needed
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model.to(device)
+
+criterion = torch.nn.BCEWithLogitsLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+
+# Train the model
+num_epochs = 100  # Adjust the number of training epochs (more epochs = more training time but with more accuracy and less loss).
+""" Training the model with more epochs
+Pros:
+- more accuracy
+- less loss (it means the model is improving)
+
+Con:
+- more training time
+"""
+
+for epoch in range(num_epochs):
+    for images in data_loader:
+        images = images.to(device)
+        labels = torch.ones((images.size(0), 1)).to(device)  # Assuming all images belong to the same class
+ 
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item()}')
+
+# Save the trained model
+torch.save(model.state_dict(), '/content/zero_shot_classification_model.pth')