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convnext_ai_detector_final.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b593b8598ef03b1f39308f3c5a8032b38ac11d1b45d8323e629facc839cd9c73
+size 785073417

model_config.json

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+{"model_name": "convnext_large.fb_in22k_ft_in1k", "img_size": 384, "mean": [0.485, 0.456, 0.406], "std": [0.229, 0.224, 0.225], "labels": {"0": "AI Generated", "1": "Human / Real"}}

predictor.py

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+
+import torch
+import timm
+from PIL import Image
+from torchvision import transforms
+
+class AIDetector:
+    def __init__(self, model_path, config_path):
+        with open(config_path, 'r') as f:
+            self.config = __import__('json').load(f)
+        
+        # Load Architecture
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = timm.create_model(self.config['model_name'], pretrained=False, num_classes=2)
+        
+        # Load Weights
+        self.model.load_state_dict(torch.load(model_path, map_location=self.device))
+        self.model.to(self.device)
+        self.model.eval()
+
+        # Setup Transforms
+        self.transform = transforms.Compose([
+            transforms.Resize((self.config['img_size'], self.config['img_size'])),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=self.config['mean'], std=self.config['std']),
+        ])
+
+    def predict(self, image_path):
+        img = Image.open(image_path).convert('RGB')
+        img_t = self.transform(img).unsqueeze(0).to(self.device)
+        
+        with torch.no_grad():
+            outputs = self.model(img_t)
+            probs = torch.nn.functional.softmax(outputs, dim=1)
+            conf, pred = torch.max(probs, 1)
+            
+        label = self.config['labels'][str(pred.item())]
+        return {"prediction": label, "confidence": conf.item()}
+
+# Example usage:
+# detector = AIDetector('convnext_ai_detector_final.pth', 'model_config.json')
+# print(detector.predict('test_image.jpg'))