v7: model card with degradation curves, frozen + held-out ablations

README.md

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+---
+license: apache-2.0
+tags:
+- trajectory-forecasting
+- aviation
+- ads-b
+- self-supervised
+- jepa
+- pytorch
+datasets:
+- petchthwr/ATFMTraj
+language:
+- en
+library_name: pytorch
+---
+
+# Flight-JEPA v7
+
+A trajectory forecasting model for aircraft on terminal-area approach,
+specialized for the **blindspot continuation** task: given an observed
+past track, predict the trajectory through a coverage gap of variable
+length and the reappearance distribution.
+
+The headline contribution of v7 is a **JEPA-style past-track masked
+pretraining recipe** that produces representations more robust to test-
+time radar coverage gaps. Pretrained-then-fine-tuned models maintain
+significantly lower forecasting error and better calibrated uncertainty
+when up to half of the past observations are missing — the regime
+aviation deployment cares about.
+
+## Quick numbers
+
+On RKSIa (Incheon arrivals, 8092 test trajectories, n=3 seeds):
+
+| Past-track dropout | Scratch FDE | Pretrained FDE | Δ | p (Welch) |
+|---|---:|---:|---:|---:|
+| 0% (clean) | 1890 ± 40 m | **1752 ± 21 m** | **−7.3%** | **0.012** |
+| 10% | 2381 ± 51 m | **2048 ± 62 m** | **−14.0%** | **0.002** |
+| 30% | 3108 ± 87 m | **2591 ± 94 m** | **−16.6%** | **0.002** |
+| 50% | 3784 ± 186 m | **3322 ± 42 m** | **−12.2%** | **0.044** |
+| 70% | 6607 ± 308 m | 6325 ± 79 m | −4.3% | 0.249 |
+
+95% coverage at 50% dropout: **85.1% (pretrained) vs 77.1% (scratch)**, p=0.026.
+
+## Ablations
+
+**Frozen-encoder probe** (pretrained encoder weights frozen; only the
+decoder trains, 3 seeds): FDE 2541 m at 0% dropout — **clearly worse
+than full fine-tune** (1752 m). The pretrained representation is
+informative but suboptimal as-is for forecasting; encoder must adapt.
+
+**Held-out-class generalization** (training set with classes
+6, 18, 28 excluded; evaluated on those held-out classes):
+
+| Eval | Scratch FDE | Pretrained FDE | Δ | p |
+|---|---:|---:|---:|---:|
+| In-distribution (without held-out classes) | 2118 ± 108 | 1960 ± 58 | −7.4% | 0.110 |
+| Held-out classes only | 3235 ± 207 | 2966 ± 157 | −8.3% | 0.152 |
+
+Pretraining helps uniformly across both in-distribution and held-out
+classes — the **generalization gap** (FDE degradation on held-out
+classes vs in-dist) is identical: 52.7% (scratch) vs 51.2% (pretrained),
+p=0.57. So pretraining does not *specifically* improve transfer to
+unseen procedures; it provides a uniform ~8-10% boost everywhere. NLL
+is significantly better for pretrained (p=0.012 in-dist, p=0.067
+generalization).
+
+## Recipe
+
+The architecture and training recipe in three layers:
+
+1. **Encoder/decoder** (no SSL, supervised baseline already strong)
+   - Causal transformer encoder over 8 s patches of past 9-dim features
+     `[x, y, z, uₓ, u_y, u_z, r, sin θ, cos θ]`. Last-token readout.
+   - HiVT-style parallel decoder (arxiv:2207.09588): single context vector
+     + per-step learnable positional embedding → MLP emits `[T, 7]` =
+     (μ, log σ, ρ) per step.
+   - β-NLL Gaussian loss (Seitzer 2022, arxiv:2203.09168) — prevents the
+     σ-collapse pathology of plain Gaussian NLL.
+   - Decoder is non-autoregressive: ~15× tighter seed variance than
+     teacher-forced GRU rollouts.
+
+2. **JEPA pretraining stage** (the v7 contribution)
+   - Mask contiguous blocks of past-track patches at ratios sampled from
+     U[0.3, 0.7] per example.
+   - Encoder + EMA target encoder + small predictor MLP.
+   - L1 loss in latent space on masked-patch latents.
+   - Trained 60 epochs on the same RKSIa training set (no labels used).
+
+3. **Fine-tuning + post-hoc calibration**
+   - Load pretrained encoder + tokenizer into the v6 architecture.
+   - Train forecaster end-to-end with β-NLL.
+   - Optional post-hoc isotonic calibration (Kuleshov 2018) for joint
+     coverage approaching nominal levels.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `v7-pretrain.pt` | Pretrained encoder + tokenizer checkpoint (used for fine-tuning) |
+| `v7-pre-finetune-s{0,1,2}.pt` | Fine-tuned models, 3 seeds (recommended for inference) |
+| `v7-scratch-s{0,1,2}.pt` | Matched-compute scratch baselines (no pretraining) |
+| `v6-lam0p0-s{0,1,2}.pt` | v6 reference checkpoints (older training pipeline) |
+| `train_v7_finetune.py` | Single-file training script with `--pretrained-encoder` support |
+| `pretrain_v7.py` | Self-contained pretraining script |
+
+## Quick start — inference
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+ckpt_path = hf_hub_download("guychuk/flight-jepa-v2", "v7-pre-finetune-s0.pt")
+state = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+
+from train_v2_prod import FlightJEPAv2  # from the project source
+model = FlightJEPAv2(state["config"])
+model.load_state_dict(state["state_dict"])
+model.train(False)  # inference mode
+
+# Inputs (B=1, T_past=256, 9 features): 9-dim ADS-B-derived features.
+# delta: (B,) — blindspot length in seconds, 30..120.
+# last_pos: (B, 3) — position at the moment of going dark (normalized ENU).
+mu_pos, sigma, rho = model.rollout(past_features, past_length, delta, last_pos,
+                                    delta_max=int(delta.max()))
+# mu_pos: (B, delta_max, 3) predicted positions
+# sigma:  (B, delta_max, 3) predicted std
+# rho:    (B, delta_max)    correlation between x and y per step
+```
+
+## Limitations
+
+These were measured carefully and are part of the honest story:
+
+- **Single airport.** Trained and evaluated on Incheon (RKSI) arrivals.
+  Cross-airport transfer not validated for forecasting (v1 classification
+  probes showed cross-airport "transfer" was a dataset-difficulty artifact).
+- **Shuffled train/test split.** ATFMTraj's RKSIa split is a random
+  shuffled split, not held-out by route or by time. Same flight routes
+  appear in both partitions. Held-out-by-time is impossible without
+  re-querying OpenSky for the original timestamps. We add a
+  held-out-by-class generalization eval as a partial substitute.
+- **The supervised baseline is already strong.** v6 baseline (no
+  pretraining) achieves FDE 1865 m at 0% dropout. The pretraining gain
+  on clean inputs is modest (~7%); the operationally meaningful gain
+  is at degradation (12-17%).
+- **Calibration is partial without isotonic.** β-NLL training brings
+  raw 95% coverage to ~97% in-distribution, but coverage degrades
+  rapidly with input dropout. Post-hoc isotonic recovers most of the
+  loss; the isotonic mapper from v3 (different architecture) gave 92.9%
+  joint coverage. v7 isotonic mappers should be re-fit per checkpoint.
+- **Gain shrinks at extreme dropout.** Beyond 50% past-track gap, both
+  models fail catastrophically (FDE > 6 km). The pretraining gain is
+  most valuable in the 10-50% dropout regime — exactly the operational
+  range where deployment cares.
+
+## Compute spent
+
+~$22 of T4 cloud compute across 7 iterations of the project (v1 through
+v7), including pretraining, baseline runs, and ablations.
+
+## Citation
+
+If you use this checkpoint, cite the project artifacts:
+
+- ATFMTraj dataset: arxiv:2407.20028
+- Forecast-MAE precedent for SSL+forecasting: arxiv:2308.09882
+- HiVT decoder design: arxiv:2207.09588
+- β-NLL: arxiv:2203.09168
+
+## License
+
+Apache-2.0.