Add the anchor-free, center-heatmap detector CLAUDE.md §3.3 specifies:
- targets.py: encodes ground-truth boxes into a per-class Gaussian
heatmap + wh regression target + center mask, using the standard
CornerNet/CenterNet gaussian-radius formulation.
- model.py: a small conv backbone (stride 4) with heatmap (sigmoid)
and wh regression heads - exactly the two outputs CLAUDE.md
specifies, sized with the 66ms/tick budget in mind.
- losses.py: modified focal loss (heatmap) + masked L1 (wh), combined
with the standard CenterNet wh_weight=0.1.
- client.py / dataset.py: pull a promoted dataset version + its set's
class list from the labeling backend and turn it into a
torch.utils.data.Dataset, reading images from local disk (same
machine as FRAMES_ROOT).
- train.py: wires it into a basic DataLoader -> train loop ->
per-epoch checkpoint.
The exact loss weighting/architecture sizing is a reasonable, standard
v1 default, not a tuned final answer - CLAUDE.md's own roadmap flags
the exact formulation as still open; this is the starting point to
iterate from.
16/16 unit tests pass on CPU with synthetic data (target/model/loss
correctness, dataset-version parsing). Beyond that, ran a real
end-to-end smoke test: live labeling backend -> promoted dataset
version -> `spelunkai-train` actually training one real epoch against
it and writing a checkpoint. Not verified: multi-epoch convergence on
real data, which needs jai's GPU and real labeled frames.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
