Update README.md

README.md

@@ -1,90 +1,44 @@
 ---
 license: cc-by-4.0
-task_categories:
-- object-detection
-language:
-- en
 tags:
 - synthetic-data
-- apple-detection
+- object-detection
 - computer-vision
-- photorealistic
-- benchmark
 - agriculture
-- yolo
+- apple-detection
+- benchmark
+- yolov8
+- domain-randomization
+language: en
+task_ids:
 - object-detection
-pretty_name: 'Better Than Real: Synthetic Apple Detection for Orchards'
-size_categories:
-- 1K<n<10K
+pretty_name: ApplesM5 Synthetic Apple Detection Benchmark
 ---
 
+# 🍎 ApplesM5: Synthetic Apple Detection Benchmark
 
-# 🍎 ApplesM5-Dataset
-
-This dataset contains annotated object detection data used in the **Synetic AI Apple Benchmark** study, measuring the effectiveness of rendered (synthetic) data versus real-world data for training small vision models. The dataset was constructed using photorealistic, physics-accurate 3D renders of apples in orchard scenes, with perfect annotations and environmental diversity.
-
-This dataset supports object detection models such as YOLOv8 and RT-DETR, and includes:
-
-- RGB images
-- Bounding box annotations (COCO format)
-- Real and rendered training/validation splits
-- Metadata for benchmark reproduction
-
-## 📊 Use Cases
-
-- Object detection
-- Real vs synthetic data performance evaluation
-- Model training and validation
-- Benchmarking data efficiency in agriculture
-
-## 📁 Dataset Structure
-ApplesM5-Dataset/
-├── images/
-│   ├── train/
-│   ├── val/
-├── annotations/
-│   ├── instances_train.json
-│   ├── instances_val.json
-├── metadata/
-│   ├── image_metadata.csv
-
-## 🔬 Benchmark Context
+This repository hosts the data files (images and annotations) used in the Synetic AI research paper, **"Better Than Real: Synthetic Apple Detection for Orchards."** This dataset was created through procedural content generation and physically-based rendering (PBR) to provide a clean, highly generalized training signal for robust agricultural AI.
 
-This dataset was used in the Synetic AI whitepaper to compare multiple training strategies:
-- Real-only
-- Rendered-only
-- Rendered + real validation
-- Joint (rendered + real) training
+The data demonstrates that training exclusively on this synthetic dataset yields superior generalization compared to models trained solely on real-world data, achieving up to a **+34.24% increase in mAP50-95**.
 
-Rendered data outperformed real data by up to **34% mAP** in certain configurations, especially at low confidence thresholds where operational reliability matters most.
+## Dataset Structure and Format
 
-## 📄 Citation & Whitepaper
+The dataset is provided in a file-based structure optimized for training YOLO models.
 
-🔗 Whitepaper coming soon. Visit [synetic.ai](https://synetic.ai) for updates.
+| Split | Description | Format | Total File Count |
+| :--- | :--- | :--- | :--- |
+| `train/` | Synthetic, procedurally generated images and labels. (Used for training.) | YOLOv8 (1 class) | > 10,000 |
+| `val/` | Real-world image samples from external orchards. (Used for validation/testing.) | YOLOv8 (1 class) | ~300 |
 
-Once published, this section will include the official citation and DOI link.
-
-## 🔧 License
-
-MIT License
-
-## 🔤 Language
-
-No language data. This dataset is image-based.
-
-## 🏷️ Tags
-
-`synthetic-data`, `object-detection`, `agriculture`, `benchmark`, `rendered`, `real-vs-synthetic`
-
-## 🎯 Task Categories
-
-- Object Detection
-
-## 📦 Size Category
-
-10K < # images < 100K
-
----
+## Citation
 
-**Contact**: For questions or commercial licensing, please visit [synetic.ai](https://synetic.ai).
+Please cite the associated whitepaper when using this dataset in your research:
 
+```bibtex
+@article{synetic2025applesm5,
+  title={{Better Than Real: Synthetic Apple Detection for Orchards}},
+  author={Blaga, Octavian and Scott, David and Zand, Ramtin and Seekings, James Blake},
+  journal={arXiv preprint arXiv:2510.xxxxx}, 
+  year={2025},
+  note={\url{[https://github.com/Syneticai/ApplesM5](https://github.com/Syneticai/ApplesM5)}}
+}