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Update README.md

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@@ -53,7 +53,7 @@ Video classification model
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  # Enable multi-GPU support
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  net = torch.nn.DataParallel(net)
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  torch.backends.cudnn.benchmark = True
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- state = torch.load(model_path, map_location=torch.device('cuda'))
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  net.load_state_dict(state['net'])
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  net.eval()
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@@ -63,7 +63,61 @@ Video classification model
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  img = img.resize((224, 224))
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  img_tensor = torch.tensor(np.array(img)).unsqueeze(0).to('cuda')
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- # Extract features from the image using the ResNet50 model
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  outputs = net(img_tensor)
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  ```
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  # Enable multi-GPU support
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  net = torch.nn.DataParallel(net)
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  torch.backends.cudnn.benchmark = True
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+ state = torch.load(model_path, map_location=torch.device('cpu'))
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  net.load_state_dict(state['net'])
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  net.eval()
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  img = img.resize((224, 224))
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  img_tensor = torch.tensor(np.array(img)).unsqueeze(0).to('cuda')
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+ # Extract features from the image
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+ outputs = net(img_tensor)
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+ ```
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+
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+ Frame classification model
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+
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+ ```python
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+ import torch
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+ from PIL import Image
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+ from model_loader import build_model
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+
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+ # Load the model
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+ net = build_model(mode='classify')
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+ model_path = 'Frame classification models'
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+
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+ # Enable multi-GPU support
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+ net = torch.nn.DataParallel(net)
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+ torch.backends.cudnn.benchmark = True
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+ state = torch.load(model_path, map_location=torch.device('cpu'))
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+ net.load_state_dict(state['net'])
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+ net.eval()
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+
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+ img_path = 'path/to/your/image.jpg'
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+ img = Image.open(img_path)
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+ img = img.resize((224, 224))
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+ img_tensor = torch.tensor(np.array(img)).unsqueeze(0).to('cuda')
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+
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+ # Extract features from the image
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+ outputs = net(img_tensor)
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+ ```
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+
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+ Non-surgical object detection model
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+
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+ ```python
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+ import torch
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+ from PIL import Image
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+ from model_loader import build_model
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+
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+ # Load the model
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+ net = build_model(mode='mask')
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+ model_path = 'Frame classification models'
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+
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+ # Enable multi-GPU support
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+ net = torch.nn.DataParallel(net)
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+ torch.backends.cudnn.benchmark = True
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+ state = torch.load(model_path, map_location=torch.device('cpu'))
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+ net.load_state_dict(state['net'])
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+ net.eval()
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+
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+ img_path = 'path/to/your/image.jpg'
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+ img = Image.open(img_path)
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+ img = img.resize((224, 224))
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+ img_tensor = torch.tensor(np.array(img)).unsqueeze(0).to('cuda')
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+
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+ # Extract features from the image
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  outputs = net(img_tensor)
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  ```
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