Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# 🌲 Trees Optimization Dataset
+### Fractional–Factorial Hyperparameter Search Results (64‑run, Resolution V DOE)
+
+This dataset contains the **experimental results** from a **64‑run fractional factorial design (2⁸⁻² Resolution V)** used to optimize hyperparameters for a **SegFormer semantic segmentation model** trained to detect trees.
+
+
+---
+
+## 📂 Dataset Structure
+
+### `results/fractional_factorial_partial.csv`
+A cumulative CSV file updated after **each experiment**.  
+It contains all completed runs so far, enabling:
+
+- real‑time monitoring  
+- ability to resume experiments  
+- incremental analysis  
+
+### `results/fractional_factorial_results.csv`
+The final CSV produced once **all 64 runs** finish.  
+It includes for each run:
+
+- experiment ID  
+- fractional‑factorial coded levels (A–H)  
+- the decoded hyperparameters  
+- best‑epoch metrics for **train**, **validation**, and **test** splits  
+- training time  
+
+Both CSV files share the same schema but differ in completeness.
+
+---
+
+## 🧪 Experimental Design Overview
+
+A **2⁸⁻² fractional factorial experiment** was used with:
+
+- **8 factors** (A–H)  
+- **64 total runs**  
+- **Resolution V**, allowing estimation of main effects and most two‑factor interactions  
+- **Generators**:  
+  - `G = A × B × C × D`  
+  - `H = A × B × E × F`
+
+Factors A–F are independent; G and H are derived.
+
+This design allows efficient exploration of a large hyperparameter space using only 64 experiments instead of 256.
+
+---
+
+## 🎛 Hyperparameter Coding
+
+Each coded factor `{ -1, +1 }` is mapped to an actual hyperparameter:
+
+| Factor | −1 Level | +1 Level |
+|--------|-----------|-----------|
+| **A** | learning rate = `1e-5` | `1e-4` |
+| **B** | weight decay = `0.0` | `0.1` |
+| **C** | scheduler = `linear` | `cosine` |
+| **D** | warmup ratio = `0.0` | `0.15` |
+| **E** | grad. accumulation = `1` | `4` |
+| **F** | epochs = `50` | `200` |
+| **G** | train batch size = `2` | `4` |
+| **H** | eval batch size = `2` | `4` |
+
+The dataset includes both the coded values and the decoded hyperparameters.
+
+---
+
+## 🤖 Model & Training Setup
+
+All experiments fine‑tune:
+
+**`nvidia/segformer-b0-finetuned-ade-512-512`**
+
+Key details:
+
+- Metrics include:  
+  - IoU  
+  - accuracy  
+  - tree‑class precision, recall, Dice  
+- Metrics are computed for train, val, and test splits  
+
+
+
+
+