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README.md

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+---
+license: other
+license_name: nutrient-commercial
+pipeline_tag: zero-shot-image-classification
+language:
+  - en
+tags:
+  - zero-shot-image-classification
+  - image-classification
+  - document-ai
+  - document-image-classification
+  - open-vocabulary
+  - clip
+datasets:
+  - nutrientdocs/doc-openvocab-benchmark
+metrics:
+  - accuracy
+---
+
+# doc-img-classification · _commercial_
+
+**Classify any document or figure image against labels you choose at runtime.** `doc-img-classification` is a
+document-adapted dual-encoder (image ↔ text) that scores an image against an *arbitrary* set of text
+labels and returns the best match — invoices, resumes, scientific papers, financial reports, receipts,
+charts, signatures, screenshots, and whatever label you type next. No fixed class list, no retraining to
+add a category: it is **open-vocabulary**.
+
+It is the document specialist in a field of generalists. General open-vocab models (CLIP, SigLIP) were
+trained on web photos; this one is adapted to documents and figures — and it shows on the
+[leaderboard](https://huggingface.co/spaces/nutrientdocs/doc-openvocab-leaderboard). **Weights are
+commercial** (not downloadable here); this page is a spec + scorecard.
+
+- 🎯 **Try it:** [doc-openvocab-demo](https://huggingface.co/spaces/nutrientdocs/doc-openvocab-demo)
+- 🏆 **Leaderboard:** [doc-openvocab-leaderboard](https://huggingface.co/spaces/nutrientdocs/doc-openvocab-leaderboard)
+- 📊 **Benchmark:** [doc-openvocab-benchmark](https://huggingface.co/datasets/nutrientdocs/doc-openvocab-benchmark)
+
+## Results
+
+On the open [doc-openvocab-benchmark](https://huggingface.co/datasets/nutrientdocs/doc-openvocab-benchmark)
+— document & figure images scored against a ~48-label open vocabulary — against the strongest general
+open-vocab image classifiers. Top-1 accuracy; higher is better.
+
+<!-- RESULTS-TABLE:START — filled from leaderboard/leaderboard.json by the scoring run (docs/PUBLISH.md Phase B) -->
+| Track | `doc-img-classification` | OpenAI CLIP L/14 | LAION CLIP-H/14 | SigLIP2 SO400M |
+| --- | ---: | ---: | ---: | ---: |
+| **Broad** (48-label open vocab) | **.880** | .726 | .654 | .801 |
+| **Specialized** (document categories) | **.712** | .463 | .250 | .536 |
+| **Synonym** (paraphrase robustness) | .730 | **.738** | .672 | .689 |
+<!-- RESULTS-TABLE:END -->
+
+The point of the benchmark: this is the open-vocabulary task — labels supplied at inference, not baked
+into a head — which a fixed-label classifier structurally cannot do. Among models that *can* do it, the
+document-adapted one leads on document and figure imagery. Full ranking on the
+[leaderboard](https://huggingface.co/spaces/nutrientdocs/doc-openvocab-leaderboard).
+
+**Concepts, not strings.** On the *synonym* track the same images are scored against held-out paraphrases
+of every label ("invoice" → "bill", "manuals" → "user guide"). Accuracy holds — the model matches the
+*concept*, so your exact wording doesn't have to match ours.
+
+## Calibrating the score
+
+Contrastive training maximizes *ranking*, which can leave the raw similarity overconfident. For a score
+you can threshold, apply **temperature scaling** — divide the logits by a fitted `T` before the sigmoid.
+It is monotonic, so ranking/accuracy is untouched and only the confidence values are repaired. On our
+serving distribution the fitted temperature drives pairwise calibration error from **0.027 → 0.002**, and
+it ships folded directly into the exported graph. Re-fit `T` if your input pipeline changes.
+
+## Intended use & limits
+
+- **Use it for:** zero-shot classification of document page images and figure/zone crops in a free-label
+  setting — the caller supplies the candidate labels. Built for the Nutrient Maestro SDK
+  image-classification contract (a single combined ONNX graph, on-prem).
+- **Limits:** optimized for **document and figure** imagery; general natural-image zero-shot ability is
+  preserved but not the focus. English label strings are the primary target.
+
+## License & data
+
+The model **weights** are offered under a commercial Nutrient license — deployed on-prem, so your
+documents never leave your infrastructure. The training set is not redistributed. The **evaluation** data
+is public and reproducible:
+[doc-openvocab-benchmark](https://huggingface.co/datasets/nutrientdocs/doc-openvocab-benchmark)
+(CC-BY-SA-4.0, test-only).
+
+> ### 📩 Get access
+>
+> `doc-img-classification` is commercial and its weights are not downloadable here. To run it on-prem —
+> open-vocabulary, calibrated, private — **contact Nutrient:
+> [nutrient.io/contact-sales](https://www.nutrient.io/contact-sales/).**
+
+## About the author
+
+<a href="https://nutrient.io/">
+  <img src="https://avatars2.githubusercontent.com/u/1527679?v=3&s=200" height="80" />
+</a>
+
+This project is maintained and funded by [Nutrient](https://nutrient.io/) - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.