README.md

---
license: cc-by-nc-4.0
pipeline_tag: image-classification
library_name: onnx
tags:
- onnx
- toy-reference
- prompt2model
- automl
- image-classification
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# prompt2model-reference-onnx

**This is a toy reference artifact, not a production model.** It is the
ONNX classification export that the public
[prompt2model-demo](https://huggingface.co/spaces/Dhi-Technologies/prompt2model-demo)
Space's conformal-abstain browser evaluates. Its purpose is to give people a
real, small, fast-loading model file to poke at while looking at how
Prompt2Model's factory pipeline (prompt to config to training to metrics to
ONNX export to report) behaves end to end, not to demonstrate classification
accuracy on any task that matters.

## What it is

- **Task:** image classification, 3 synthetic classes: `red_square`,
  `blue_circle`, `green_triangle`.
- **Backbone:** `mobilenet_v3_small`, ImageNet-pretrained (the smoke test
  path hardcodes a pretrained backbone as of
  [PR #18](https://github.com/DHI-Technologies-Inc/Prompt2Model-Language-Guided-Vision-Model-Factory/pull/18)).
- **Parameters:** 1,520,931 (about 1.52M).
- **Input:** 96x96 RGB images.
- **Export format:** ONNX, opset 17.
- **Training data:** a tiny synthetic toy set (colored shapes), 2 epochs,
  batch size 8, `low_light` augmentation tag, generated by the same run that
  produced this export. Not a natural-image dataset. See
  [prompt2model-examples](https://huggingface.co/datasets/Dhi-Technologies/prompt2model-examples)
  for the matching example fixtures.

## Measured metrics (verbatim, this run only)

This is the deterministic smoke run after PR #18 (seeded RNGs, stratified
splits, pretrained backbone), the same artifact the demo Space evaluates.

```json
{
  "accuracy": 1.0,
  "macro_f1": 1.0,
  "latency_ms": 78.61008350737393,
  "fps": 12.721014345522176,
  "parameter_count": 1520931,
  "calibration": {
    "calibrated": true,
    "temperature": 0.05,
    "alpha": 0.1,
    "conformal_threshold": 0.004888,
    "ece_before": 0.3864,
    "ece_after": 0.0014,
    "val_samples": 7
  }
}
```

- **Chance level on this 3-class set is 0.33.** Accuracy 1.0 / macro F1 1.0
  is expected and not impressive: the toy set is tiny (a handful of
  synthetic images per class), the task is trivially separable (three fixed
  colored shapes), and the split is small enough that a perfect score
  carries very little statistical weight. Treat these numbers as "the
  pipeline plumbing works end to end," not "this model classifies well."
- Latency/FPS were measured locally on a development machine (Apple
  Silicon Mac, ONNX Runtime CPU execution provider) via the same
  `onnxruntime.InferenceSession` the model ships in, not a production or
  edge-device benchmark.
- Calibration numbers are from the pipeline's conformal-abstain stage
  (temperature scaling + conformal threshold), which is what the demo
  Space's "abstain browser" actually visualizes.

## What this is not

- Not a production or deployment-ready classifier.
- Not evidence of real-world classification quality: the label set,
  images, and split are all synthetic and tiny.
- Not tuned, ablated, or compared against any baseline beyond the trivial
  chance rate.

## Provenance

- **Repo:** `DHI-Technologies-Inc/Prompt2Model-Language-Guided-Vision-Model-Factory`
- **Commit:** `ba62c2fd721115f996812af61079ad339a8c5b80` ("fix(training): seed
  RNGs, stratify splits, use pretrained backbone in smoke test (#18)")
- **Command:** `.venv/bin/python -m prompt2model.cli smoke-test --output-dir output/smoke`
- **Date:** 2026-07-10

## Links

- Example dataset: [Dhi-Technologies/prompt2model-examples](https://huggingface.co/datasets/Dhi-Technologies/prompt2model-examples)
- Live demo: [Dhi-Technologies/prompt2model-demo](https://huggingface.co/spaces/Dhi-Technologies/prompt2model-demo)

## License and access

Released under `cc-by-nc-4.0` and gated for non-commercial research and
evaluation only. No redistribution. Commercial licensing via
[dhi-tech.com](https://dhi-tech.com).