Update README.md

README.md

@@ -54,7 +54,7 @@ This model **outperforms the fine-tuned YOLOv11x reported in the MVBroTrack pape
 
 ---
 
-## 📊 Performance
+##  Performance
 
 ### Final metrics (validation set — 3 706 images, imgsz 640)
 
@@ -82,7 +82,7 @@ This model **outperforms the fine-tuned YOLOv11x reported in the MVBroTrack pape
 
 ---
 
-## 🥊 Comparison with the reference paper
+##  Comparison with the reference paper
 
 **Reference paper** — Cardoen et al., *"Multi-camera detection and tracking for individual broiler monitoring"*, *Computers and Electronics in Agriculture*, 2025 (MVBroTrack).
 
@@ -102,11 +102,11 @@ Why is this model better on the unified benchmark:
 3. **AdamW + warmup + cosine-like LR decay** (paper uses SGD).
 4. **Close-mosaic scheduling** (last 10 epochs) for cleaner fine-tuning endgame.
 
-> ⚠️ Direct comparison note: our 79.3 % is measured on the PoultryVision Unified validation split, which is broader than the paper’s MVBroTrack-only test set. The ≥ 70.8 % number remains a meaningful reference point because the paper authors report it as the best single-view detector on broilers; our model handles both broilers **and eggs** and still surpasses it overall.
+>  Direct comparison note: our 79.3 % is measured on the PoultryVision Unified validation split, which is broader than the paper’s MVBroTrack-only test set. The ≥ 70.8 % number remains a meaningful reference point because the paper authors report it as the best single-view detector on broilers; our model handles both broilers **and eggs** and still surpasses it overall.
 
 ---
 
-## 🚀 Quick start
+##  Quick start
 
 ```bash
 pip install ultralytics huggingface_hub
@@ -156,7 +156,7 @@ model.export(format="tflite", int8=True)               # Edge / Coral
 
 ---
 
-## 🧠 Full pipeline (beyond single-view detection)
+##  Full pipeline (beyond single-view detection)
 
 This model is the single-view detection stage of a larger pipeline inspired by the MVBroTrack paper:
 
@@ -170,7 +170,7 @@ The full reference implementation of modules 2-5 is shipped as [`poultry_vision_
 
 ---
 
-## 📁 Files in this repo
+##  Files in this repo
 
 | File                                  | Description                               |
 |---------------------------------------|-------------------------------------------|
@@ -190,7 +190,7 @@ The full reference implementation of modules 2-5 is shipped as [`poultry_vision_
 
 ---
 
-## 🧪 Training recipe (excerpt)
+##  Training recipe (excerpt)
 
 ```yaml
 model:       yolo11m.pt
@@ -225,7 +225,7 @@ See `args.yaml` for the complete set.
 
 ---
 
-## ⚖️ License
+##  License
 
 - **Model weights**: **AGPL-3.0** (inherited from Ultralytics YOLOv11).
   Commercial deployments without open-sourcing your full stack should acquire an [Ultralytics Enterprise License](https://www.ultralytics.com/license).
@@ -233,14 +233,14 @@ See `args.yaml` for the complete set.
 
 ---
 
-## 📚 Citation
+##  Citation
 
 If you use this model or the PoultryVision dataset, please cite:
 
 ```bibtex
 @misc{williamsanderson_poultryvision_2025,
   title  = {PoultryVision: A YOLOv11m Model and Unified Dataset for Broiler and Egg Detection},
-  author = {Williams Anderson},
+  author = {Stephane Williams Anderson ASSA},
   year   = {2025},
   howpublished = {\url{https://huggingface.co/Williamsanderson/PoultryVision}},
 }
@@ -259,7 +259,7 @@ And the reference paper this work is based on:
 
 ---
 
-## 🙏 Acknowledgements
+##  Acknowledgements
 
 - **Ultralytics** for the YOLOv11 architecture and training framework.
 - **Cardoen et al.** for MVBroTrack (multi-camera broiler dataset, calibration, tracking ground truth).