diff --git a/PYTHON/image_detection.py b/PYTHON/image_detection.py
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index 0000000..adda08a
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+++ b/PYTHON/image_detection.py
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+from PIL import Image
+from ultralytics import YOLO
+import os
+# Load a pretrained YOLOv8n model
+model = YOLO('yolov8n.pt')
+#THIS IS FOR IMAAAAAAGEEEEEEEEEE DETECTION !!
+img_count = 0
+image_paths = []
+image_directory = "F:\\GFG\\Images\\" #enter the path for image folder here !
+for filename in os.listdir(image_directory):
+    if filename.endswith(".jpg") or filename.endswith(".JPG") or filename.endswith(".jpeg") or filename.endswith(".png"):
+        image_paths.append(os.path.join(image_directory, filename))
+        img_count += 1
+# Now, image_paths contains the paths of the images
+print(f'Total number of images in the folder {image_directory} : ',img_count)
+# # For example, if you have a directory with multiple image files:
+# image path example : 'F:\\GFG\\Images\\img3.jpg'
+count = 0
+for i in image_paths:
+    results = model(i)  # results list
+    count += 1
+    # Show the results
+    for r in results:
+        im_array = r.plot()  # plot a BGR numpy array of predictions
+        im = Image.fromarray(im_array[..., ::-1])  # RGB PIL image
+        im.show()  # show image
+        im.save(f'F:\\GFG\\static\\detection_result\\{count}.jpg')  # save image
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