horos 0.2.0.dev0

Quickstart

Install (details & Jetson notes below):

pip install horos   # lightweight core: datasets, annotation, web UI — no torch
horos install       # ML stack (torch / rfdetr / transformers), matched to your machine
horos doctor        # verifies the environment; --fix installs what's missing

pip install horos deliberately ships without the ML stack: the right torch build depends on your platform (Windows needs a CUDA index, Jetson needs the JetPack wheel, GPU-less Linux wants the 2 GB-smaller CPU build) and pip cannot make that call. horos install detects your GPU and installs the right builds; ML commands check the environment on startup and tell you exactly what to run if something is missing or mis-built.

Run the whole pipeline from the terminal:

horos init my-project                   # new project directory
horos import my-project path/to/data    # COCO / YOLO / VOC / Darknet / VIA, dir or zip
horos ui my-project                     # web UI: dataset, annotate, train, evaluate

Or from Python — every UI action has a scriptable twin:

import horos.api as api

project = api.open_project("my-project")
record = api.start_training(project, api.TrainRunConfig(model="rfdetr-small"))
# ... poll api.training_status(project, record.run_id) ...
report = api.get_eval_report(project, record.run_id, "test")

Web UI

horos ui <project> serves four pages on localhost.

Dataset

Import by dropping a zip (COCO / YOLO / VOC / Darknet / VIA — format is auto-detected), get a validation report with actionable errors, per-class statistics, and train/valid/test re-splitting.

Annotate

A keyboard-first canvas for boxes and polygons. OWLv2 turns text prompts into zero-shot pre-labels, so annotators start from correcting instead of from a blank image — with an accept / fix / reject review flow, and safe concurrent annotation for teams.

Train

One click to start: hyperparameters are derived from your dataset's statistics with the reasoning shown, and every value can be overridden. Live loss/mAP curves, a run queue with in-place editing, resume with full optimizer state, OOM auto-backoff, a selectable best-checkpoint criterion, and a post-run verdict with concrete suggestions.

Evaluate

Drop photos, GIFs, or videos onto a trained model and browse per-frame predictions in a gallery viewer (confidence slider, frame-by-frame navigation). COCO metrics with per-class AP and PR curves, persisted per run.

Models

All registered weights are Apache-2.0. Nothing is bundled — weights download on first use and cache locally.

Detection (trainable)

Model	Params	Input	Notes
RF-DETR Nano	30.5 M	384 px	fastest — Jetson-friendly real-time
RF-DETR Small	32.1 M	512 px	fast — good default for Jetson
RF-DETR Medium	33.7 M	576 px	balanced accuracy/latency
RF-DETR Large	129 M	704 px	highest accuracy — desktop GPU recommended

Annotation assistants (not for deployment)

Model	Params	Role
OWLv2 Base / Large	155 M / 437 M	open-vocabulary zero-shot pre-labeling from text prompts
SAM ViT-B	94 M	turns autolabel boxes into polygon masks

RF-DETR XL/2XL are deliberately unregistered: their weights are not Apache-2.0 (PML 1.0). Loading them requires an explicit acknowledge_non_apache=True.

Platform support

Capability	Ubuntu (CUDA)	Windows	macOS	Jetson
Dataset management & annotation	✅	✅	✅	✅
Auto-labeling (OWLv2)	✅	✅	✅ (MPS/CPU, slower)	✅
Training	✅	✅	small-dataset validation only	discouraged, not blocked
Inference & evaluation	✅	✅	✅	✅
TensorRT export (planned)	✅	✅	❌ refused explicitly	✅

Unsupported combinations raise a clear error at the API layer and show up as disabled buttons with an explanation in the UI — never a silent CPU fallback. Device priority: CUDA → MPS → CPU, recorded in each run's metadata.

Installation

Two steps, on every platform:

pip install horos   # the core — datasets, annotation, web UI (no ML deps)
horos install       # the ML stack, matched to this machine

horos install detects your OS, NVIDIA driver and CUDA version and runs the right pip commands (--dry-run shows them first, --cpu forces the CPU build). horos doctor re-checks everything and plans the same fixes — it also catches the classic trap of a CPU-only torch sitting on a GPU machine.

What horos install decides for you

Platform	torch source
Linux + NVIDIA GPU	PyPI (Linux wheels bundle CUDA)
Linux without GPU	PyTorch CPU index (saves ~2 GB)
macOS	PyPI universal build (MPS)
Windows + NVIDIA GPU	PyTorch index matching your driver's CUDA (cu118 … cu132) — the PyPI Windows wheel is CPU-only
Windows without GPU	PyPI (CPU)
Jetson	never pip-installed — see below

For Linux/x86_64 CI and containers where the default PyPI torch is already right, pip install horos[ml] installs the same stack in one shot.

The repo also ships bootstrap scripts that create ./.venv, install the core, and run horos install for you:

./install.sh        # Ubuntu / macOS / Jetson
install.bat         # Windows

Use a dedicated environment. horos pins rfdetr exactly (upstream has had silent annotation-corruption bugs; reproducibility wins) and requires transformers >= 5.1 — installing into a shared ML environment will upgrade transformers, supervision, huggingface-hub and friends, which can break other projects living in that environment.

Jetson (read this — it matters)

On Jetson, torch must come from NVIDIA's JetPack-matched wheel — the PyPI torch has no CUDA support there. pip install horos is safe (the core has no torch dependency), and horos install never pip-installs torch on Jetson: it prints the JetPack steps, installs rfdetr with --no-deps so pip can never swap torch out, and adds the training stack once the JetPack torch is in place. horos also warns at backend load time when it detects a Jetson platform where torch.cuda.is_available() is False.

Use a venv created with --system-site-packages so the JetPack torch stays visible (./install.sh does this automatically on Jetson):

pip install horos
# torch/torchvision: install the NVIDIA wheel matching your JetPack version —
# https://docs.nvidia.com/deeplearning/frameworks/install-pytorch-jetson-platform/
horos install       # rfdetr (--no-deps), training stack, transformers

Roadmap

• Project & dataset core — formats, validation, stats, splits
• Manual annotation — bbox + polygon, multi-annotator
• Auto-labeling — OWLv2 open-vocabulary, review workflow
• Training — derived hyperparameters, queue, resume, live monitoring
• Evaluation — media gallery, COCO metrics, per-class analysis
• Error analysis — confusion pairs, worst-case mining
• Experiment management — run comparison, dataset fingerprints
• Export & deploy — ONNX / TensorRT / TFLite, model cards, parity checks

Development

python -m venv .venv && . .venv/bin/activate
pip install -e .[dev]      # the core is torch-free by design
horos install              # ML stack — needed for the backend/training tests
pytest tests/test_invariants.py && pytest

tests/test_invariants.py runs first for a reason: it statically enforces the architecture — model dependencies live only in horos/backends/, import horos never drags in torch, and the UI talks to the core exclusively through the web API. Models are adapters; the workflow is the product.

License

Distributed under the Apache License 2.0. Model weights are downloaded at runtime and cached locally — horos never bundles or redistributes them, and each model's license is recorded in the registry, shown in the UI, and stamped into every training run.

Acknowledgments

RF-DETR by Roboflow · OWLv2 by Google Research · Segment Anything by Meta AI