YOLO11SegmentationRetailJune 22, 2026·3 min read
Supermarket items segmentation and counting with YOLO11
Discover how to achieve advanced items segmentation in supermarkets with Ultralytics YOLO11 for efficient object detection and analysis.
M
Muhammad Rizwan Munawar
Computer Vision Engineer · Founder, Rizwan AI
Discover how to leverage the power of Ultralytics YOLO11 to achieve precise object segmentation and counting. In this guide, you'll learn step-by-step how to use YOLO11 to streamline processes, enhance accuracy, and unlock new possibilities in computer vision applications.
System specifications used to create this project
- CPU: Intel® Core™ i5-10400 CPU @ 2.90GHz for optimal performance.
- GPU: NVIDIA RTX 3050 for real-time processing.
- RAM & Storage: 64 GB RAM and 1 TB SSD for large datasets.
- Model: Fine-tuned YOLO11 for items segmentation in trolley.
- Dataset: Custom-labeled supermarket images.
How to perform items segmentation and counting
Step 1: Train or fine-tune YOLO11
To get started, you can train the YOLO11 model on a custom dataset tailored to your specific use case. However, if the pre-trained YOLO11 model already performs well for your application, there's no need for customization, you can directly use the pre-trained weights for faster and efficient deployment. Explore the full details in the Ultralytics Documentation.
Step 2: How to draw the segmentation masks
# Install ultralytics package
# pip install ultralytics
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
from collections import Counter
model = YOLO(model="path/to/model/file.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH,
cv2.CAP_PROP_FRAME_HEIGHT,
cv2.CAP_PROP_FPS))
vwriter = cv2.VideoWriter("instance-segmentation4.avi",
cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
while True:
ret, im0 = cap.read()
if not ret:
break
object_counts = Counter() # Initialize a counter for objects detected
results = model.track(im0, persist=True)
annotator = Annotator(im0, line_width=3)
if results[0].boxes.id is not None and results[0].masks is not None:
masks = results[0].masks.xy
track_ids = results[0].boxes.id.int().cpu().tolist()
clss = results[0].boxes.cls.cpu().tolist()
boxes = results[0].boxes.xyxy.cpu()
for mask, box, cls, t_id in zip(masks, boxes, clss, track_ids):
if mask.size>0:
object_counts[model.names[int(cls)]] += 1
color = colors(t_id, True)
mask_img = im0.copy()
cv2.fillPoly(mask_img, [mask.astype(int)], color)
cv2.addWeighted(mask_img, 0.7, im0, 1 - 0.7, 0, im0)
annotator.seg_bbox(mask=mask, mask_color=color,
label=str(model.names[int(cls)]),
txt_color=annotator.get_txt_color(color))
vwriter.write(im0)
cv2.imshow("Ultralytics FastSAM", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
vwriter.release()
cap.release()
cv2.destroyAllWindows()
Step 3: Count segmented objects
Now, we have already drawn the object masks, we can now count the objects.
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
from collections import Counter
model = YOLO(model="path/to/model/file.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH,
cv2.CAP_PROP_FRAME_HEIGHT,
cv2.CAP_PROP_FPS))
vwriter = cv2.VideoWriter("instance-segmentation4.avi",
cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))
while True:
ret, im0 = cap.read()
if not ret:
break
object_counts = Counter() # Initialize a counter for objects detected
results = model.track(im0, persist=True)
annotator = Annotator(im0, line_width=3)
if results[0].boxes.id is not None and results[0].masks is not None:
masks = results[0].masks.xy
track_ids = results[0].boxes.id.int().cpu().tolist()
clss = results[0].boxes.cls.cpu().tolist()
boxes = results[0].boxes.xyxy.cpu()
for mask, box, cls, t_id in zip(masks, boxes, clss, track_ids):
if mask.size>0:
object_counts[model.names[int(cls)]] += 1
color = colors(t_id, True)
mask_img = im0.copy()
cv2.fillPoly(mask_img, [mask.astype(int)], color)
cv2.addWeighted(mask_img, 0.7, im0, 1 - 0.7, 0, im0)
annotator.seg_bbox(mask=mask, mask_color=color,
label=str(model.names[int(cls)]),
txt_color=annotator.get_txt_color(color))
# Display total counts in the top-right corner
x, y = im0.shape[1] - 200, 30
margin = 10
for i, (label, count) in enumerate(object_counts.items()):
text = f"{label}={count}"
font_scale = 1.4
font_thickness = 4
padding = 15
text_size, _ = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX,
font_scale, font_thickness)
rect_x2 = im0.shape[1] - 10
rect_x1 = rect_x2 - (text_size[0] + padding * 2)
y_position = y + i * (text_size[1] + padding * 2 + 10)
if y_position + text_size[1] + padding * 2 > im0.shape[0]:
break
rect_y1 = y_position
rect_y2 = rect_y1 + text_size[1] + padding * 2
cv2.rectangle(im0, (rect_x1, rect_y1), (rect_x2, rect_y2),
(255, 255, 255), -1)
text_x = rect_x1 + padding
text_y = rect_y1 + padding + text_size[1]
cv2.putText(im0, text, (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX,
font_scale, (104, 31, 17), font_thickness)
vwriter.write(im0)
cv2.imshow("Ultralytics FastSAM", im0)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
vwriter.release()
cap.release()
cv2.destroyAllWindows()
It's time to watch the Output 🚀
Applications in retail
- Smart Inventory Management: Categorize items and track movement effortlessly.
- Retail Analytics: Gain insights into customer behavior and product popularity.
Explore more
Transform your retail operations with YOLO11 today! 🚀