🕐🔥 temporal-model

Monolithic repository for the Pyronear temporal smoke classifier: train it, evaluate it, and serve it behind an API.

The model is a per-tube smoke classifier: a YOLO detector proposes boxes, boxes are linked into temporal tubes (greedy IoU), each tube's frames are cropped to 224×224 patches and scored by a ViT (DINOv2) backbone + transformer head that emits one logit per tube.

Packages

Four independent packages, each with its own pyproject.toml and tests/.

Path	Import	Purpose
core/	temporal_model.core	Model, tube building, patch extraction, inference, packaging.
train/	temporal_model.train	DVC training pipeline. Depends on core.
eval/	temporal_model.eval	DVC evaluation pipeline (packaged-model protocol metrics). Depends on core.
api/	temporal_model.api	FastAPI serving layer, shipped as a Docker service. Depends on core.

train/eval/api depend on core via a uv path source (temporal-model-core = { path = "../core", editable = true }). core and train pull in PyTorch / timm / ultralytics, so their first uv sync is large.

Quick start

make install        # uv sync across all four packages
make test           # pytest across all four packages
make lint           # ruff check across all four packages

Per package, cd <pkg> && make install|lint|format|test.

Run the API locally (Docker)

make serve      # API + MinIO via docker compose, http://localhost:8000 (GET /health)

Equivalent to cd api && docker compose up --build. The compose stack ships local-dev MinIO defaults (bucket frames, minioadmin creds); drop a model.zip under api/models/ for /health to report model_loaded: true.

Origin

Ported from the Pyronear vision-rd research repo's bbox-tube-temporal work:

• core/ — from lib/bbox-tube-temporal
• train/ and eval/ — from experiments/temporal-models/bbox-tube-temporal