Datasets:

juangamerosalinas
/

trees-optimization

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