diff --git a/ML Project/image_detection.py b/ML Project/image_detection.py
deleted file mode 100644
index adda08a0..00000000
--- a/ML Project/image_detection.py	
+++ /dev/null
@@ -1,27 +0,0 @@
-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
\ No newline at end of file