Update README.md

README.md

@@ -7,4 +7,44 @@ 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()