Product Detection In Shelf Yolov8

🚀 YOLOv8 Shelf Object Detection in Retail Environments

This YOLOv8 Shelf Object Detection model is a powerful tool for retail environments. It can accurately detect objects on shelves, count inventory, and help optimize shelf layouts, enhancing the overall retail experience.

🚀 Quick Start

To use the YOLOv8 Shelf Object Detection model, follow these steps:

First, install the necessary libraries:

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43

Then, use the following Python code to load the model and start detection:

# Wave your wand (or keyboard) to get started!
from ultralyticsplus import YOLO, render_result
import cv2

# Cast a spell to summon the model
model = YOLO('foduucom/shelf-object-detection-yolov8')

# Tweak the magical parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = "path/to/your/shelf/image"

#you can pass live camera streaming or video
# Begin the mystical journey through video frames
# (Remember to have your retail tapestry ready)
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Unleash the magic of YOLOv8
        results = model(frame)

        # Showcase the magic on the frame
        annotated_frame = results[0].plot()

        # Present the enchanted frame
        cv2.imshow("YOLOv8 Retail Wizardry", annotated_frame)

        # Dispel the spell if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Return to reality when the video ends
        break

# Release your captive video and close the portal
cap.release()
cv2.destroyAllWindows()

✨ Features

Model Enthusiasm 🎉

This model is designed to bring a new level of efficiency and fun to retail environments. It can transform the way you manage your shelves and interact with customers.

Model Magic ✨

The YOLOv8 Shelf Object Detection model has excellent object detection capabilities. It can quickly and accurately find various products on shelves, and it's also good at counting inventory, helping you keep your shelves well - stocked.

Supported Labels 🏬

The model supports the following labels:

['Empty Shelves', 'Magical Products']

Collaboration Love ❤️

We welcome collaboration! If you like this model, give it a thumbs up 👍. Let's work together to improve the retail industry.

📚 Documentation

Uses

Direct Use

Integrate this model into your retail system for real - time inventory management, shelf optimization, and automated restocking.

Downstream Wonder

Use this model to optimize shelf layouts, understand product placement, and enhance the customer experience.

Not - So - Magic Disclaimers ⚡

• The model may be affected by poor lighting conditions and messy shelf arrangements. Keep your shelves tidy for better results.
• Rapid changes in product types and shelf configurations can impact the model's accuracy.

Human Touch & Wizard Wisdom 🧙

Test the model in your specific retail environment before full - scale deployment.

🔧 Technical Details

The YOLOv8 Shelf Object Detection model has a precision (mAP@0.5(box)) of 0.91.

📄 License

No license information is provided in the original document.

Model Masters 🧙‍♂️

The YOLOv8 Shelf Object Detection model was developed by wizards at FODUU AI.

@ModelCard{
  author    = {Nehul Agrawal and
               Pranjal Singh Thakur},
  title     = {YOLOv8 Shelf Object Detection in Retail Environments},
  year      = {2023}
}

If you have any questions or want to contribute, send an email to info@foduu.com.