Auspex RT

Bounding-box object detection. Train on your own boxes, detect boxes. One task, done properly — no segmentation heads, no keypoints, nothing you are not using taking up parameters and compute.

Supported annotation formats: YOLO / Roboflow text, COCO JSON, CVAT-for-images 1.1 XML, Pascal VOC XML — mixed freely in one training run.

Trained from random initialisation. No pretrained weights are downloaded, and none are required: the model is yours from the first step, with no upstream licence attached to it.

Install

pip install auspex-rt        # import name: auspex_rt

Wheels for Linux x86_64, Windows, and macOS Apple Silicon, Python 3.10–3.13. An NVIDIA GPU is recommended for training (precision is picked automatically for your hardware); CPU works for inference.

Use

from auspex_rt import Auspex

model = Auspex(labelspace="labelspace.yaml")
model.train(data="data.yaml", epochs=150)          # train on your own data
results = model.predict("images/", save=True)      # detect

model = Auspex(weights="best.pt")                  # checkpoints are self-contained
model.val(data="data.yaml")

Or the console command: auspex-rt train --data data.yaml --labelspace labelspace.yaml, auspex-rt predict --weights best.pt --source images/, auspex-rt val --weights best.pt --data data.yaml.

Pointing it at a YOLO export

A Roboflow/YOLO export is described by its data.yaml, which is the only file in it that names the classes. Point the parser straight at that:

sources:
  - name: batch1
    path: /datasets/pow/data.yaml   # YOLO data.yaml
    split: train
    provides: {rect: true}

Both YOLO line formats are read: the 5-field box form (cls cx cy w h) and the polygon form (cls x1 y1 … xn yn) that segmentation and oriented-box exports produce. A polygon is converted to its axis-aligned bounding box, and the conversion is counted and warned about — this model detects axis-aligned rectangles, so a polygon carries information it cannot represent, and a silent lossy import is how you conclude the model is broken when the loader threw the shape away.

Pre-labeling workflow

Send model predictions straight back to your labeling tool:

model.predict("images/", export="cvat")     # CVAT-for-images 1.1 XML pre-labels
model.predict("images/", export="coco")     # or COCO JSON
model.predict("huge_scans/", tiles="auto")  # tile very large images so small
                                            # objects stay detectable

Training quality features: a val split in data.yaml enables validation during training (best.pt tracks real validation AP, per-class AP is logged, --patience N stops early); --class-balance oversamples images containing rare classes; auspex-rt calibrate --weights best.pt --data data.yaml stores per-class confidence thresholds in the checkpoint so classes with different score scales all show up at their own best operating point.

Very small objects

If your objects are only a few pixels across at training resolution, the default detection grid (finest stride 8) cannot resolve them. Two options, in a train.yaml:

model:
  strides: [4, 8, 16, 32]   # adds a finer detection level
train:
  micro_batch: 2            # ~4x the anchors; halve the batch at 768 px
augment:
  native_crop_p: 0.5        # or: train on zoomed windows of large source images

Measured on a set where half the objects were smaller than one grid cell, the finer level roughly doubled F1 on the smallest classes. It is a trade, not a free win: long thin objects (road-like lines spanning much of the frame) tend to fragment into several detections, so leave it off for those. Both settings are off by default — check your own box sizes before reaching for them.

Data configuration

labelspace.yaml — your categories, in order; the index is the class id.

data.yaml — one entry per annotation source:

sources:
  - name: batch1
    path: annotations/batch1.xml   # YOLO data.yaml | CVAT 1.1 XML | COCO .json | VOC xml dir
    images_root: images/
    split: train
    provides: {rect: true}

provides: {rect: true} declares that this source actually labels boxes. A source that does not is still useful — its images teach background — but it must not push predictions toward background on classes it never labelled, and this flag is what prevents that.

A dataset annotated with polygons or keypoints still trains a box model: the box is derived from the annotation's extent, so a segmentation set you already own is usable without re-annotation.

Growing a model over time: --transfer <checkpoint> continues from existing weights even when classes were added or reordered; --source-balance rebalances very unequal sources; --cache-records keeps memory flat on large datasets.

Trial and licensing

Training is free for 30 days, in full — no key, no sign-up, no feature limits. The clock starts on your first training run.

Prediction, validation, calibration and export are never gated. They keep working after the trial ends, on any model you have already trained, forever. Evaluating this against your own data does not require buying anything.

After 30 days, training needs a credential. Either kind goes in the same place:

export AUSPEX_RT_LICENSE=AUSPEXRT-...   # a licence key, or
export AUSPEX_RT_LICENSE=HLN1....       # an account token
                                        # or write it to ~/.auspex-rt/license.key
  • A licence key (AUSPEXRT-...) is issued to an organisation for a fixed term. Request one at the link below.
  • An account token (HLN1....) is one you generate yourself, from the console of an account that has been granted access to this package.

Both are verified offline against a public key compiled into the wheel — nothing phones home, so training works on air-gapped machines and this never sits in the critical path of your build. The trade is that neither can be withdrawn before it expires, which is why both are dated.

Get a key: https://heliontechltd.com/license

License

Proprietary software under an End-User License Agreement: licensed users may install and run Auspex RT and train models on their own data (the resulting weights are theirs); copying, redistribution, modification, and reverse engineering are prohibited. See https://heliontechltd.com/license for terms and licensing inquiries.

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Credits

Author: Md. Sohanur Islam Shuvo

License

AUSPEX END-USER LICENSE AGREEMENT (EULA)

Requires

Python >=3.10

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The following attestation bundles were made for auspex_rt-0.1.0-cp310-cp310-macosx_11_0_arm64.whl:

Publisher: wheels.yml on sohanurislamshuvo/auspex-rt

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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