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license: apache-2.0
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# 🌲 Tree Segmentation Performance 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.


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## 📂 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.

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## 🧪 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.

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## 🎛 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.

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## 🤖 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