lofop 0.1.1

LOFOP: a modular, enterprise-grade computer vision framework.

LOFOP

LOFOP is a modular, enterprise-grade computer vision framework built on PyTorch, with its own original detector: LOFOP-Detect. It is an independent design and implementation that follows modern computer-vision engineering practices while remaining self-contained.

Status: Phases 1-5 complete — core engine, data subsystem, cross-platform native ops, LOFOP-Detect models, training engine, and ONNX + TensorRT export. 196 tests passing. See docs/architecture.md for the full roadmap and per-phase status.

📖 New here? Read the Operator's Manual — a complete, step-by-step guide to installing, training, exporting, deploying, and troubleshooting LOFOP.

What works today

• Datasets — COCO, YOLO, and VOC support through one canonical model: any-to-any conversion, validation (degenerate/out-of-bounds boxes, missing files, dangling categories), and statistics.
• LOFOP-Detect — an original anchor-free detector (RidgeNet backbone, DeltaFusion neck with attention only on the cheap stride-32 level, ApexHead with an IoU-quality branch, dynamic top-k label assignment). Variants are pure config: n = 1.3M params, s = 3.8M, ex = 20.1M. Design + trade-offs: docs/lofop-detect.md.
• Python SDK — from lofop import Detector: build, train, predict (boxes in original image coordinates), evaluate, and export through one documented class. Full reference: docs/sdk.md.
• Training — AMP, EMA weights, warmup+cosine schedule, gradient clipping, atomic checkpointing with resume, event-bus lifecycle hooks, and a COCO-protocol evaluator (mAP@50, mAP@50:95, precision, recall).
• Cross-platform native ops — IoU and class-aware NMS kernels (20-200x over pure Python) with a verified-identical Python fallback, so a compiler is never required. The C++ path builds with g++/clang on Linux/macOS and MinGW/clang/MSVC on Windows; lofop.ops.backend() reports which is active.
• Benchmarking — lofop benchmark renders the standard metric table (mAP, FPS, params, FLOPs, model size) and never prints a number that was not actually measured.
• ONNX + TensorRT export — lofop export writes a numerically verified ONNX graph (network + box decoding), or a TensorRT engine (--format tensorrt --fp16) via that same ONNX; postprocess_dense finishes inference torch-free with the C++ NMS, so serving hosts need only a runtime + the LOFOP core. Details: docs/deploy.md.
• Deployment scaffolding — CPU / CUDA / ONNX Runtime Docker images (docker/).

Installation

pip install lofop            # from PyPI
yay -S lofop                 # Arch Linux (AUR)

Optional feature sets (extras):

pip install "lofop[models]"      # + PyTorch, for lofop.models / lofop.training
pip install "lofop[deploy]"      # + onnx, onnxruntime, for ONNX export
pip install "lofop[all]"         # models + deploy in one go
python -c "from lofop.ops import build_native; build_native()"   # optional C++ fast path

From a source checkout instead:

pip install -e ".[dev]"          # framework + dev tools (pytest, ruff, build, twine)

Requires Python 3.9+. The core and data layers run without PyTorch (edge/CI friendly); torch attaches only to the model and training subsystems. Maintainer release steps live in docs/packaging.md.

Quickstart

Dataset tools (no torch needed):

lofop dataset convert  --from coco --source instances.json --to yolo --target out/
lofop dataset validate --format yolo --source out/            # exit 1 on errors
lofop dataset stats    --format coco --source instances.json -o stats.md

Train and benchmark (five-minute CPU demo that fills the metric table end to end):

python examples/train_shapes.py --epochs 30 --workdir runs/shapes
lofop benchmark --config lofop/configs/lofop-detect/n.yaml --config lofop/configs/lofop-detect/s.yaml -o table.md
lofop export --config lofop/configs/lofop-detect/n.yaml --checkpoint runs/shapes/checkpoints/best.pt -o model.onnx
lofop export --config lofop/configs/lofop-detect/n.yaml --format tensorrt --fp16 -o model.engine   # NVIDIA GPU

Measured on the fixed-protocol benchmark (benchmarks/quality_benchmark.py, 30 CPU epochs, 128px shapes): mAP@50 0.92, best F1 0.90, 0.8 false positives/image at conf 0.25, 115 FPS end-to-end predict. Accuracy on a real dataset awaits a full GPU training run — the protocol is documented in docs/lofop-detect.md, and the table renders - until numbers are measured.

Python SDK — the one-import path (full reference):

from lofop import Detector

det = Detector("lofop-detect-ex", num_classes=2, class_names=["cat", "dog"])
det.train(data_format="coco", train_source="train.json", image_root="images/", epochs=100)
for hit in det.predict("photo.jpg"):                  # boxes in original image coordinates
    print(hit.boxes, hit.scores, hit.labels)
det.export("model.onnx")

Lower-level control remains fully public — registries, Config, Trainer, and the deploy functions are the same objects the SDK uses:

from lofop import Config, HUB
import lofop.models                                   # registers model components

cfg = Config.load("lofop/configs/lofop-detect/s.yaml")
model = HUB.build(cfg.model)                          # ready LofopDetect

Custom components plug in without touching the framework:

from lofop.registries import BACKBONES

@BACKBONES.register()
class MyNet: ...
# then in YAML:  backbone: {type: backbone/MyNet, ...}

Repository layout

lofop/
  core/          # registry, config, events, plugins, logging, exceptions (torch-free)
  data/          # canonical dataset model, COCO/YOLO/VOC adapters, validator, statistics
  models/        # LOFOP-Detect: RidgeNet, DeltaFusion, ApexHead, losses, assigner
  training/      # trainer, EMA, checkpoints, torch data bridge, COCO-protocol evaluator
  deploy/        # ONNX + TensorRT export, torch-free post-processing
  ops/ + csrc/   # native C++ IoU/NMS with Python fallback
  utils/         # model benchmarking (metric table, FLOPs, FPS)
  sdk.py         # high-level Python SDK: the Detector class (docs/sdk.md)
  configs/       # packaged model family definitions (n, s, ex)
  cli.py         # `lofop` command: dataset / train / benchmark / export
configs/         # training config examples
docker/          # CPU, CUDA, and ONNX Runtime images
docs/            # architecture, per-module references, LOFOP-Detect design doc
benchmarks/      # reusable performance measurement scripts
examples/        # end-to-end runnable demos
tests/           # pytest suite mirroring the package layout (177 tests)

Development

python -m pytest                                   # run the test suite
ruff check lofop tests benchmarks examples         # lint
python benchmarks/bench_core.py -o report.md       # core engine micro-benchmarks
python benchmarks/bench_ops.py                     # C++ vs Python ops speedups
python benchmarks/bench_detect.py                  # detector params + latency

Every push and pull request runs the full test suite (Python 3.9/3.11/3.12, native C++ ops built) and lint via GitHub Actions (.github/workflows/ci.yml), and a distribution build check, so main stays releasable.

Roadmap

Next phases: inference sources (video/RTSP/webcam), OpenVINO engines, and REST serving. The full subsystem map with per-phase status lives in docs/architecture.md.

License

Apache License 2.0. See LICENSE.