juangamerosalinas/trees-optimization

🌲 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.

📂 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:

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