load image with resnet

train/resnet01.py

@@ -0,0 +1,27 @@
+import torch
+import torchvision.models as models
+from PIL import Image
+import torchvision.transforms as transforms
+
+model = models.resnet50(pretrained=True)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+model = model.to(device)
+
+image_path = '1706678996799-2.jpeg'
+image = Image.open(image_path).convert('RGB')
+
+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]),
+])
+
+input_data = transform(image).unsqueeze(0).to(device)
+
+model.eval()
+
+with torch.no_grad():
+    output = model(input_data)
+
+print(output)