---
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](https://github.com/ultralytics/ultralytics)
- **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

```python
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()