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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
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