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diff --git a/object-detection.py b/object-detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..09884b355d2ce46846d3baa8a543085a7b59eb03
--- /dev/null
+++ b/object-detection.py
@@ -0,0 +1,159 @@
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+import cv2
+import os
+import numpy as np
+import time
+import random
+
+class CNN(nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        self.features = nn.Sequential(
+            nn.Conv2d(3, 16, 3, padding=1), nn.BatchNorm2d(16), nn.ReLU(), nn.MaxPool2d(2, 2),
+            nn.Conv2d(16, 32, 3, padding=1), nn.BatchNorm2d(32), nn.ReLU(), nn.MaxPool2d(2, 2),
+            nn.Conv2d(32, 64, 3, padding=1), nn.BatchNorm2d(64), nn.ReLU(), nn.MaxPool2d(2, 2),
+            nn.Conv2d(64, 128, 3, padding=1), nn.BatchNorm2d(128), nn.ReLU(), nn.MaxPool2d(2, 2),
+            nn.Conv2d(128, 256, 3, padding=1), nn.BatchNorm2d(256), nn.ReLU(), nn.MaxPool2d(2, 2),
+            nn.Conv2d(256, 512, 3, padding=1), nn.BatchNorm2d(512), nn.ReLU(), nn.MaxPool2d(2, 2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(512 * 3 * 3, 512),
+            nn.ReLU(),
+            nn.Dropout(0.5),
+            nn.Linear(512, num_classes)
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = self.classifier(x)
+        return x
+
+
+class WebcamPredictor:
+    def __init__(self, model_path):
+        # Check for CUDA availability
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Using device: {self.device}")
+
+        # Load the saved model
+        checkpoint = torch.load(model_path, map_location=self.device, weights_only= True)
+        self.class_names = checkpoint['class_names']
+
+        # Initialize model
+        self.model = CNN(num_classes=len(self.class_names))
+        self.model.load_state_dict(checkpoint['model_state_dict'])
+        self.model.to(self.device)
+        self.model.eval()
+
+        # Define image transformations
+        self.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])
+        ])
+
+    def predict_frame(self, frame):
+        try:
+            img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            img = cv2.resize(img, (224, 224))
+
+            cv2.imshow("prediction", img)
+            mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)
+            std = np.array([0.229, 0.224, 0.225], dtype=np.float32)
+            img = img.astype(np.float32) / 255.0
+            img = (img - mean) / std
+            img = np.transpose(img, (2, 0, 1))
+            img_tensor = torch.from_numpy(img).unsqueeze(0).float().to(self.device)
+            # Predict
+            with torch.no_grad():
+                output = self.model(img_tensor)
+                probabilities = torch.nn.functional.softmax(output, dim=1)
+                predicted_idx = torch.argmax(probabilities, dim=1).item()
+                confidence = probabilities[0][predicted_idx].item()
+                predicted_class = self.class_names[predicted_idx]
+
+            return predicted_class, confidence
+
+        except Exception as e:
+            print(f"Error during prediction: {str(e)}")
+            return None, None
+
+    def start_webcam(self):
+        # Initialize webcam
+        cap = cv2.VideoCapture(0)
+
+        # Check if webcam is opened successfully
+        if not cap.isOpened():
+            print("Error: Could not open webcam")
+            return
+
+        # Set webcam properties (optional)
+        cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
+        cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
+
+        print("Webcam started. Press 'q' to quit.")
+
+        # Variables for FPS calculation
+        fps_start_time = time.time()
+        fps_frame_count = 0
+        fps = 0
+
+        while True:
+            # Read frame from webcam
+            ret, frame = cap.read()
+            if not ret:
+                print("Error: Could not read frame")
+                break
+
+            # Calculate FPS
+            fps_frame_count += 1
+            if fps_frame_count >= 30:
+                fps = fps_frame_count / (time.time() - fps_start_time)
+                fps_start_time = time.time()
+                fps_frame_count = 0
+
+            # Make prediction
+            predicted_class, confidence = self.predict_frame(frame)
+
+            # Draw prediction and FPS on frame
+            if predicted_class is not None:
+                # Draw semi-transparent background for text
+                overlay = frame.copy()
+                cv2.rectangle(overlay, (10, 10), (400, 90), (0, 0, 0), -1)
+                alpha = 0.6
+                frame = cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 0)
+
+                # Draw text
+                font = cv2.FONT_HERSHEY_SIMPLEX
+                cv2.putText(frame, f"Class: {predicted_class}", (20, 40), font, 0.8, (0, 255, 0), 2)
+                cv2.putText(frame, f"Confidence: {confidence:.2%}", (20, 70), font, 0.8, (0, 255, 0), 2)
+                cv2.putText(frame, f"FPS: {fps:.1f}", (20, 100), font, 0.8, (0, 255, 0), 2)
+
+            # Display frame
+            cv2.imshow('Webcam Prediction', frame)
+
+            # Check for 'q' key to quit
+            if cv2.waitKey(30) & 0xFF == ord('q'):
+                break
+
+        # Release resources
+        cap.release()
+        cv2.destroyAllWindows()
+
+
+def main():
+    # Model path - update this to your model's path
+    model_path = "saved_models/best_model.pth"
+
+    # Initialize predictor and start webcam
+    predictor = WebcamPredictor(model_path)
+    predictor.start_webcam()
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/requirement.txt b/requirement.txt
new file mode 100644
index 0000000000000000000000000000000000000000..97f14b7d0f25d6a154c8b418c231e9a6927a75cc
--- /dev/null
+++ b/requirement.txt
@@ -0,0 +1,4 @@
+torch
+torchvision
+numpy
+Pillow
diff --git a/saved_models/best_model.pth b/saved_models/best_model.pth
new file mode 100644
index 0000000000000000000000000000000000000000..02229d87513544a58a4d81565bf6930ba78109ba
--- /dev/null
+++ b/saved_models/best_model.pth
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:15bbc8d56961e4ef232f007e1f3c0fab5f61e3d52154832e116f72b41b79c04c
+size 47324608