metadata
language:
- en
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
tags:
- planogram
- shelf-gaps
🛒 Retail Shelf Gap Detection Model
This is a YOLOv8-based object detection model fine-tuned on the Shelf Images for Planograms dataset to detect gaps in retail shelf arrangements. The model aims to assist in planogram compliance checking by identifying empty spaces on shelves and computing compliance scores.
📌 Use Case
This model is intended for retail analytics teams to:
- Detect shelf gaps in retail store images.
- Quantify compliance against a predefined planogram.
- Evaluate image quality and calculate gap-related metrics.
🧠 Model Details
- Base Model: Ultralytics/YOLOv8
- Task: Object Detection
- Trained On: Shelf Images for Planograms Dataset (2095 images)
- Framework: PyTorch via Ultralytics
🧪 Metrics and Scoring Breakdown
The model is used as part of a retail shelf scoring pipeline that computes:
- Gap Score (50%)
- Image Quality Score (30%)
- Gap Density Score (20%)
- Final Compliance Score = Weighted sum of the above
🛠️ How to Use
from ultralytics import YOLO
from huggingface_hub import hf_hub_download
# Download model
model_path = hf_hub_download(repo_id="akul-29/Retail-Shelf-Gap-Detection_Model", filename="best.pt")
model = YOLO(model_path)
# Run inference
results = model("path_to_your_image.jpg")
results.show()