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