# Training

**Status tags:** `IMPLEMENTED` · `VERIFIED` · `MEASURED` · `ATTEMPTED` · `NOT RUN` · `REJECTED`.

The project trains **small modules on frozen backbones**. No backbone is fine-tuned end-to-end. Every
hyperparameter lives in `configs/base.yaml` (no magic numbers in Python), and every trained artifact
records the frozen config hash it was trained against.

---

## 1. Overview

| Artifact | Backbone (frozen) | Where trained | Selection signal |
|---|---|---|---|
| `router` adapter | `all-MiniLM-L6-v2` | local CPU | val (group-split) |
| `grounding` head | RemoteCLIP ViT-B/32 | local | val fraction 0.10 |
| `change` head | STANet ResNet-18 + PAM | local | val (LEVIR split) |
| `optical_sar` fusion head | CROMA-base | local, **seed sweep** | held-out test (pre-registered) |
| `change_vqa` head | over cached change features | **external GPU (Kaggle)** | val answer accuracy |
| `vlm` LoRA adapter | SmolVLM-500M-Instruct | **external GPU** | frozen 1000-question subset |

**CPU-first.** The router and every head except the VLM adapter train on CPU. The VLM LoRA adapter
requires a GPU (T4-class).

## 2. Router adapter

A **50,822-parameter** adapter over the frozen MiniLM encoder.

| Hyperparameter | Value |
|---|---|
| epochs | 60 |
| batch size | 64 |
| learning rate | 0.001 |
| weight decay | 0.01 |
| task loss weight | 1.0 |
| modality loss weight | 0.3 |
| binary loss weight | 0.5 |
| val ratio | 0.15 |
| hard negatives to test | true |

**Finding F4-2 — the encoder is frozen, so embeddings are cached** and the adapter trains on cached
vectors. **Measured: 20 epochs / 4,096 vectors in 0.28 s on CPU.** No GPU is required.

**Finding F4-3 — splits are by GROUP** (template / hard-negative family), never by example.
Hard-negative families are placed in the **test** split so their accuracy measures generalisation
rather than memorisation.

> **Status:** the adapter is trained and shipped. Its measured number (0.965116) is **validation-only,
> ungated, n = 86**; the router **test split was NOT RUN**.

## 3. Grounding head

A trainable head over the frozen RemoteCLIP ViT-B/32 encoder.

| Hyperparameter | Value |
|---|---|
| learning rate | 0.0001 |
| batch size | 16 |
| epochs | 20 |
| weight decay | 0.0001 |
| warmup ratio | 0.05 |
| grad clip | 1.0 |
| val fraction | 0.10 |
| save every steps | 500 |
| box loss weight | 0.5 |
| GIoU loss weight | 0.3 |
| confidence loss weight | 0.2 |
| `positive_confidence_weight` | **20.0** |

**Architecture constraint (enforced, not documented).** Per-cell feature is
`concat([patch, text, patch·text, global_pool]) = 4 × 512 = 2048`. `core/config.py` **rejects any
value other than** `4 × grounding.encoder_projected_dim` at load time, and the specialist asserts the
same 512 against the real model — because a mismatch is a *silent* shape error that torch only raises
at the similarity step, after patch features are already cached.

**Why `positive_confidence_weight = 20.0`.** Objectness BCE sees ~1 positive cell out of 49.
Unweighted, the optimum is "no object" everywhere; the weight is what stops that collapse.

**Resolution is frozen at 224.** 448 was evaluated and **REJECTED** (paired test: mean diff −0.0147,
95 % CI [−0.0160, −0.0134], t = −22.63, at 1.59× latency).

## 4. Change head

STANet-style Siamese detector.

| Hyperparameter | Value |
|---|---|
| encoder | ResNet-18 |
| self-attention | **PAM** (BAM alternative not used) |
| tile size | 256 |
| tile overlap | 0 |
| threshold | 0.50 |
| min component pixels | 32 |
| learning rate | 0.001 |
| batch size | 8 |
| BCE weight | 0.5 |
| Dice weight | 0.5 |

Trained on LEVIR-CD-256 (train 7120 / val 1024 / test 2048). **This is the only task with a
`VERIFIED` headline metric** (pooled IoU 0.8122 on the immutable test split).

## 5. Optical-SAR fusion head

| Hyperparameter | Value |
|---|---|
| input dim | 2318 = 3 × 768 + 12 + 2 |
| hidden dim | 512 |
| dropout | 0.2 |
| num classes | 19 (BigEarthNet CLC) |

**Seed sweep.** Training was run as two arms (**armA**, **armB**) × five seeds (**100–104**), with a
per-arm variance report (`armA_seed_variance_report.json`). The **production head** is a distinct,
frozen artifact (`fusion_head_production_v001/head.pt`) with its own
`production_head_record.json` and a `phase12_rerun_verification.json`.

**Pre-registration.** The headline metric is a **pre-registered** 115-class protocol
(`pre_registered_11.5`) computed over the 19-class label space on the held-out test split. The
metric JSON records `is_deciding_statistic: False`, i.e. it is a reported measurement, not a
decision statistic.

**Feature caches.** Training consumes cached CROMA features
(`fusion_features/`, `fusion_features_armB/`, ~231 MB each). These caches are **reproducible** and are
not released as model weights.

## 6. Change-VQA head — trained externally

The change-VQA head was trained **outside this repository**, on an external GPU (Kaggle), following
`docs/R02_KAGGLE_TRAINING_GUIDE.md`. That guide's status on entry was
`IMPLEMENTATION_READY_FOR_EXTERNAL_TRAINING`, and its explicit contract is:

> **Training produces an artifact, not a verified capability, and the run record says
> `TRAINED_UNVERIFIED`.**

The returned checkpoint was **promoted** through a byte-identity gate
(`artifacts/change_vqa/run/PROMOTION.json`):

| Property | Value |
|---|---|
| sha256 | `cfae5e43b97ca930f568dc5b8ae4f36b24e9ff717af226159802206ffd63a82a` |
| bytes | 5,822,809 |
| architecture | `change_vqa_head_v1` |
| parameters | 1,453,912 |
| non-finite tensors | **0** |
| weights modified during promotion | **false** |
| byte-identical to source | **true** |
| hash agrees across | `model_metadata.json`, `run_record.json`, `hashes.json` |

**Selection:** epoch **8**, chosen on **Val answer accuracy = 0.700018**, stopped by early stopping.
Seed 42. It trains on **cached change + text features** (specs `change_feat_v1`,
`change_cache_spec c801326f85a185f8`, `text_cache_spec d2801ea1a314354a`), not on raw imagery.

> **Note.** The raw CDVQA loader (see [`DATASETS.md`](DATASETS.md)) loads examples but has **no
> training loop**; the shipped head is a cached-feature model. These are different paths and are not
> conflated.

## 7. VLM LoRA adapter — trained externally

A PEFT LoRA adapter on **frozen** `HuggingFaceTB/SmolVLM-500M-Instruct`.

| Hyperparameter | Value |
|---|---|
| PEFT version | **0.19.1** |
| `r` (rank) | 16 |
| `alpha` | 32 |
| `dropout` | 0.05 |
| target modules | `model.text_model.*.{q,k,v,o,gate,up,down}_proj` |
| precision | **fp16** (finding C-6: T4 is SM 7.5 → **fp16, NOT bf16**) |
| batch size | 2 |
| gradient accumulation | 8 |
| learning rate | 0.0002 |
| epochs | 1 |
| gradient checkpointing | true |
| save every steps | 500 |

**Finding F5-2 (cost).** The processor's default `longest_edge` is 2048, which upscales 512-px tiles
4× and then splits them into **17 sub-images** (`pixel_values (1, 17, 3, 512, 512)`, 1142 prompt
tokens). Pinning `processor_longest_edge: 512` yields `pixel_values (1, 1, 3, 512, 512)`. The plan
estimated a 4× cost overrun; the **measured** figure is ~17×.

**Finding F5-3.** SmolVLM requires one `<image>` token per image in the prompt; hand-written prompt
strings raise `ValueError`. Prompts are always built through `processor.apply_chat_template()`.

**Outcome:** metrics usable (exact_match 0.963, F1 0.96432, +49.5 pp) but the artifact is
**ACCEPTANCE-REJECTED** for promotion. The deployed caption/VQA path uses the **unadapted** model.
See [`MODELS.md`](MODELS.md) §3.6.

## 8. Reproducibility contract for training

- **Seed 42** everywhere (`project.seed`).
- **Precision `fp16`** (T4 constraint), never bf16.
- Every artifact records the **frozen config hash** `78f1e3700da15aa1`; a config edit moves the hash
  and invalidates the artifact.
- `save_every_steps: 500`; checkpoints are archived as provenance, not released.
- Training guides state their own entry status and **never** claim a trained artifact is a verified
  capability.

## 9. What was NOT trained

| Item | State |
|---|---|
| Backbone fine-tuning (any) | **NOT DONE** — all backbones frozen |
| Router on the test split | **NOT RUN** |
| Any end-to-end / joint training | **NOT RUN** |
| Re-training of the change head at a second resolution | **NOT RUN** |
| Benchmark adapters | **NOT RUN** |