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| interface Row { | |
| model: string; | |
| params: string; | |
| f1: string; | |
| iou: string; | |
| ap: string; | |
| note?: string; | |
| } | |
| // Real LEVIR-CD test results (threshold selected on val, applied to test) — from the committed | |
| // comparison in docs/results/. This is the genuine project story; the served ONNX may be a | |
| // placeholder until the trained bundles are staged. | |
| const ROWS: Row[] = [ | |
| { model: "FC-Siam-diff (baseline)", params: "0.83M", f1: "0.886", iou: "0.796", ap: "0.932" }, | |
| { | |
| model: "Siamese-SegFormer / MiT-b2 (diff)", | |
| params: "24.72M", | |
| f1: "0.911", | |
| iou: "0.836", | |
| ap: "0.943", | |
| note: "ImageNet-pretrained strong model", | |
| }, | |
| { | |
| model: "DINOv2-base frozen linear-probe", | |
| params: "1.64M", | |
| f1: "0.889", | |
| iou: "0.800", | |
| ap: "0.924", | |
| note: "frozen FM features only", | |
| }, | |
| { | |
| model: "DINOv2-base + LoRA", | |
| params: "2.82M", | |
| f1: "0.913", | |
| iou: "0.839", | |
| ap: "0.946", | |
| note: "foundation-model tier — headline", | |
| }, | |
| ]; | |
| export default function ModelCard() { | |
| return ( | |
| <div className="card-page"> | |
| <div className="card-inner"> | |
| <p className="card-eyebrow">Model card · Track A · high-res aerial</p> | |
| <h2>Siamese change detection on LEVIR-CD</h2> | |
| <p className="card-lead"> | |
| Weight-shared Siamese change-detection models on 0.5 m RGB aerial imagery. Two dates of | |
| the same place go in; a per-pixel building-change map comes out. Three tiers are compared on | |
| the identical LEVIR-CD test split through one evaluation harness. | |
| </p> | |
| <table className="metrics-table"> | |
| <thead> | |
| <tr> | |
| <th>Model</th> | |
| <th>Trainable</th> | |
| <th>F1</th> | |
| <th>IoU</th> | |
| <th>AP</th> | |
| </tr> | |
| </thead> | |
| <tbody> | |
| {ROWS.map((r) => ( | |
| <tr key={r.model} className={r.note?.includes("headline") ? "row-headline" : ""}> | |
| <td> | |
| {r.model} | |
| {r.note && <span className="row-note">{r.note}</span>} | |
| </td> | |
| <td className="num">{r.params}</td> | |
| <td className="num">{r.f1}</td> | |
| <td className="num">{r.iou}</td> | |
| <td className="num">{r.ap}</td> | |
| </tr> | |
| ))} | |
| </tbody> | |
| </table> | |
| <h3>The defensible claim — parameter efficiency</h3> | |
| <p className="muted"> | |
| DINOv2-base + LoRA reaches F1 0.913 with only <strong>2.82M</strong> trainable | |
| parameters versus the SegFormer strong model's <strong>24.72M</strong> at F1 0.911 | |
| — a statistical tie on accuracy (within noise) at ~9× fewer trainable params. The frozen | |
| linear-probe (1.64M, decoder only) already ≈ the baseline, so the self-supervised | |
| representation carries most of the change signal and LoRA supplies the adaptation lift. | |
| </p> | |
| <h3>Honest limitations</h3> | |
| <ul className="muted"> | |
| <li> | |
| Per-scene F1 has high variance (mean ≈ 0.77, std ≈ 0.31, min 0.00). The foundation model | |
| lifts the mean but does <em>not</em> fix the hardest small/subtle-change tiles. | |
| </li> | |
| <li> | |
| Overall pixel accuracy is deliberately not reported: change is a tiny pixel fraction, so | |
| "predict no change" scores ~99% and is meaningless. Metrics are change-class only. | |
| </li> | |
| <li>Trained weights inherit LEVIR-CD research/non-commercial terms — showcase use only.</li> | |
| </ul> | |
| <h3>Domain gap — why the live mode is a different model</h3> | |
| <div className="callout"> | |
| <p className="muted"> | |
| These models are trained on 0.5 m aerial imagery and do <strong>not</strong> transfer | |
| to 10 m Sentinel-2. The live-AOI mode (a later milestone) uses a Sentinel-2-native | |
| model so it produces meaningful output on satellite scenes — the domain split is a design | |
| decision, not an afterthought. | |
| </p> | |
| </div> | |
| </div> | |
| </div> | |
| ); | |
| } | |