MODEL PROFILE

vit-historical-page

ufal · vit-historical-page

Fine-tuned image classifiers that sort scanned archive pages into eleven content categories, so that each page can be sent to the processing it needs: OCR for printed or typed text, handwriting recognition for manuscripts, table extraction for forms, image extraction for drawings and photographs. They look at the page image only, so the language of the page does not matter. The code, the command line and the HTTP service are in atrium-page-classification [^3]. Every version is a **revision** (branch) of this repository [^1]. They share the eleven categories and differ in their training data and base model: ViT [^2] [^6] [^7], EfficientNetV2 [^8] [^9] [^11] or RegNetY [^12]. The current generation is **`vX.4`**: five base models, retrained on the licence-compatible public dataset [^10]. **Name the revision when you load a model:** `main` still holds `v4.3`, the generation before. Each model was scored on the test partition of its own fold of the dataset (folds 1, 1, 2, 2 and 5; 4,791–4,795 pages each), on 22 October 2026. The five together, averaged, are the code's ensemble (`--best`, and the default of its HTTP service).

Context windowNot disclosed
Input → Output图像 → 待核验
Model weightsNot verified
Profile updated2026/11/2 19:16:25
OVERVIEW

Model overview

Fine-tuned image classifiers that sort scanned archive pages into eleven content categories, so that each page can be sent to the processing it needs: OCR for printed or typed text, handwriting recognition for manuscripts, table extraction for forms, image extraction for drawings and photographs. They look at the page image only, so the language of the page does not matter. The code, the command line and the HTTP service are in atrium-page-classification [^3]. Every version is a **revision** (branch) of this repository [^1]. They share the eleven categories and differ in their training data and base model: ViT [^2] [^6] [^7], EfficientNetV2 [^8] [^9] [^11] or RegNetY [^12]. The current generation is **`vX.4`**: five base models, retrained on the licence-compatible public dataset [^10]. **Name the revision when you load a model:** `main` still holds `v4.3`, the generation before. Each model was scored on the test partition of its own fold of the dataset (folds 1, 1, 2, 2 and 5; 4,791–4,795 pages each), on 22 October 2026. The five together, averaged, are the code's ensemble (`--best`, and the default of its HTTP service).

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。