Add files using upload-large-folder tool

.gitattributes

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 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
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+synthetic/metadata/ood_infer.json filter=lfs diff=lfs merge=lfs -text
+synthetic/metadata/train.jsonl filter=lfs diff=lfs merge=lfs -text
+synthetic/metadata/id_need_infer.json filter=lfs diff=lfs merge=lfs -text
+synthetic/metadata/train_sft.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,235 @@
+---
+license: apache-2.0
+language:
+  - en
+  - zh
+task_categories:
+  - image-classification
+  - image-feature-extraction
+tags:
+  - font-recognition
+  - visual-font-recognition
+  - VFR
+  - open-set
+  - vision-language
+  - multimodal
+  - typography
+size_categories:
+  - 100K<n<1M
+pretty_name: DesignVFR
+---
+
+# DesignVFR
+
+> **Towards Universal Open-Set Visual Font Recognition via Augmented Synthetic Similarity** &nbsp;·&nbsp; CVPR 2026 Findings
+
+**DesignVFR** is the first large-scale benchmark for **universal open-set Visual Font Recognition (VFR)**. While prior VFR work is limited to closed-set classification on isolated character-level grayscale images, DesignVFR covers font recognition in real-world *universal* scenarios — sentences, complex backgrounds, and artistic effects across **posters, films, slides and vlogs** — and explicitly evaluates the **open-set** regime where unseen fonts keep being added.
+
+- 📰 **Paper**: *Towards Universal Open-Set Visual Font Recognition via Augmented Synthetic Similarity* (CVPR 2026 Findings)
+- 💻 **Code**: [github.com/your-org/FontVLM](https://github.com/your-org/FontVLM)
+- 🐍 **Loader**: anchors all paths on `${DATASET_ROOT}`, see *Quick Start*
+
+## ✨ Highlights
+
+| | |
+|---|---|
+| **Total fonts** | 1,242 multilingual fonts (Chinese + Latin + multi-script) |
+| **Total images** | 668,400 augmented synthetic + 42,794 real-world universal-scenario |
+| **Real-world splits** | 19,759 poster crops · 22,414 video frames |
+| **Augmentation pipeline** | Sentence-level rendering with font-faithful augmentations (color, blur, perspective, background, …) |
+| **Open-set protocol** | 1,068 in-domain (ID) fonts + 174 out-of-domain (OOD) fonts, never seen during training |
+
+## 📦 Subsets
+
+DesignVFR is organised into three complementary subsets:
+
+| Subset | Source | What's inside |
+|---|---|---|
+| **`synthetic/`** | Augmented synthetic pipeline | Training set (1,087 fonts) · ID gallery (1,069) · OOD gallery (197) · ID/OOD query splits |
+| **`posterreal/`** | Real-world graphic-design posters | 221 fonts, sentence-level crops with rich layouts |
+| **`videoreal/`** | Real-world video frames | 148 ID fonts + 170 OOD fonts; sentence-level frames from films / vlogs |
+
+## 🗂️ Layout (after `python unpack.py`)
+
+```
+DesignVFR/
+├── synthetic/
+│   ├── train/                       1,087 font dirs · 332,100 images
+│   ├── id_infer/                    1,069 font dirs (synthetic ID gallery)
+│   ├── id_need_infer/               1,069 font dirs (synthetic ID query)
+│   ├── ood_infer/                     197 font dirs (synthetic OOD gallery)
+│   ├── ood_need_infer/                197 font dirs (synthetic OOD query)
+│   └── metadata/
+│       ├── train.jsonl                       # ms-swift training data (next-token format)
+│       ├── train_with_mask.jsonl             # variant with text-region masks
+│       ├── train_sft.jsonl                   # SFT-style variant (font name as response)
+│       ├── id_infer.json / id_need_infer.json
+│       ├── ood_infer.json / ood_need_infer.json
+│       └── font_family_to_index.json         # 1,068-class label map
+├── posterreal/
+│   ├── images/                     221 font dirs (real poster crops)
+│   └── metadata/
+│       ├── id.json                 # ID protocol (fonts overlap with synthetic train)
+│       └── ood.json                # OOD protocol (unseen fonts)
+└── videoreal/
+    ├── id/                         148 font dirs
+    ├── ood/                        170 font dirs
+    └── metadata/
+        ├── id.json
+        └── ood.json
+```
+
+> **All paths inside the metadata files use the `${DATASET_ROOT}` placeholder**, e.g.  
+> `"${DATASET_ROOT}/synthetic/train/AaHouDiHei/AaHouDiHei_normal_400_0.png"`.  
+> Set `DATASET_ROOT` once and the metadata works no matter where you put the dataset on disk.
+
+## 🚀 Quick Start
+
+### 1. Download
+
+```bash
+# requires huggingface_hub>=0.24
+huggingface-cli download <user>/DesignVFR --repo-type dataset \
+    --local-dir ./DesignVFR
+```
+
+### 2. Unpack the tar shards
+
+The dataset is shipped as ~1 GB tar shards (`synthetic/train.part-001.tar`, …) to stay friendly to git-LFS. Extract them in place with the bundled unpacker:
+
+```bash
+cd DesignVFR
+python unpack.py
+```
+
+After this step the on-disk layout matches the diagram above. The original tar shards can be safely deleted.
+
+### 3. Point your training code at it
+
+```bash
+export DATASET_ROOT=/abs/path/to/DesignVFR
+```
+
+### 4. Use it
+
+#### a. As a `datasets.Dataset` (raw metadata)
+
+Each metadata JSON is a list of records, each containing the image URL plus its label:
+
+```python
+import json, os
+from datasets import Dataset
+
+records = json.load(open('DesignVFR/posterreal/metadata/id.json'))
+# Expand ${DATASET_ROOT}
+for r in records:
+    r['text_img_url'] = os.path.expandvars(r['text_img_url'])
+
+ds = Dataset.from_list(records)
+print(ds[0])
+# {'font_family': 'Source Han Sans SC',
+#  'text': '欢迎咨询',
+#  'text_img_url': '/abs/path/.../posterreal/images/Source Han Sans SC Regular/...png',
+#  ...}
+```
+
+#### b. With ms-swift (training out of the box)
+
+The training jsonl follows ms-swift's *messages* format and uses a special `<|font|>` token as the assistant response:
+
+```jsonl
+{"images": ["${DATASET_ROOT}/synthetic/train/AaHouDiHei/AaHouDiHei_normal_400_0.png"],
+ "messages": [
+   {"role": "user", "content": "<image> What is the font of the text in this image?"},
+   {"role": "assistant", "content": "<|font|>"}
+ ],
+ "label": 0}
+```
+
+ms-swift's preprocessor automatically expands `${DATASET_ROOT}` at load time (see `swift/llm/dataset/preprocessor/core.py::_cast_mm_data`), so you only need:
+
+```bash
+export DATASET_ROOT=/abs/path/to/DesignVFR
+swift sft \
+    --model Qwen/Qwen2.5-VL-3B-Instruct \
+    --dataset $DATASET_ROOT/synthetic/metadata/train.jsonl \
+    ...
+```
+
+## 🔢 Statistics at a glance
+
+| Split | # fonts | # images | Type |
+|---|---:|---:|---|
+| `synthetic/train`           | 1,087 | 332,100 | Augmented synthetic, training |
+| `synthetic/id_infer`        | 1,069 |  55,350 | Augmented synthetic, ID gallery |
+| `synthetic/id_need_infer`   | 1,069 |  55,350 | Augmented synthetic, ID query |
+| `synthetic/ood_infer`       |   197 |  68,050 | Augmented synthetic, OOD gallery |
+| `synthetic/ood_need_infer`  |   197 |  12,700 | Augmented synthetic, OOD query |
+| `posterreal/id`             |   ~70 |   9,875 | Real posters, ID protocol |
+| `posterreal/ood`            |  ~150 |  10,505 | Real posters, OOD protocol |
+| `videoreal/id`              |   148 |  10,045 | Real video frames, ID |
+| `videoreal/ood`             |   170 |  12,369 | Real video frames, OOD |
+
+(Per-split counts are derived from the released metadata files; "# fonts" reflects unique font directories.)
+
+## 📑 Metadata schema
+
+### `synthetic/metadata/{train, train_with_mask, train_sft}.jsonl`
+
+| Field | Type | Notes |
+|---|---|---|
+| `images` | `list[str]` | Single image URL with `${DATASET_ROOT}` prefix |
+| `messages` | `list[{role, content}]` | ms-swift conversation format |
+| `label` | `int` | Index into `font_family_to_index.json` (0 ≤ label < 1068) |
+
+### `synthetic/metadata/{id,ood}_{infer,need_infer}.json`, `posterreal/metadata/*.json`, `videoreal/metadata/*.json`
+
+| Field | Type | Notes |
+|---|---|---|
+| `text_img_url` | `str` | Crop image, anchored on `${DATASET_ROOT}` |
+| `mask_img_url` | `str` | (synthetic only) Binary mask of the text region |
+| `font_family` | `str` | Logical font family (used as the **gold label**) |
+| `font_file` | `str` | Concrete font file (multiple files may share one family) |
+| `text` | `str` | Rendered text content |
+| Other | — | Style fields (color, weight, …) only present on `posterreal` |
+
+### `synthetic/metadata/font_family_to_index.json`
+
+`{"<font_family_name>": <int_index>}` — the canonical 1,068-class label map used during synthetic-set training.
+
+## 🧪 Open-set protocol
+
+We split fonts into two disjoint pools:
+
+- **In-Distribution (ID)** — 1,068 fonts seen during synthetic training. `*_id` query splits should report top-k accuracy against an ID gallery of size 1,068.
+- **Out-of-Distribution (OOD)** — 174 fonts **never** seen during training. `*_ood` query splits target the open-set capability of the recogniser.
+
+The accompanying paper recommends two evaluation modes:
+
+1. **Classification mode** — direct softmax over 1,068 ID classes (only meaningful on ID splits).
+2. **Similarity mode** — extract a query feature, match it via cosine similarity against a *synthetic reference gallery* built from `*_infer` splits. This naturally extends to OOD fonts at inference time, **without retraining**.
+
+See the [FontVLM repository](https://github.com/your-org/FontVLM) for reference implementations of both.
+
+## ⚖️ License & responsible use
+
+- The dataset is released under the **Apache 2.0** license, **for research purposes only**.
+- The font files themselves are **not redistributed** — only rendered images are. Some fonts shipped in DesignVFR carry restrictive commercial licenses (e.g. fonts whose names contain *"Non-Commercial Use"*); please consult the original font foundries before any commercial application.
+- Real-world poster / video frames were collected from public sources for academic study only. If you are a copyright holder and would like a sample removed, please open an issue on the GitHub repository.
+
+## 📚 Citation
+
+```bibtex
+@InProceedings{Zhou_2026_CVPR,
+    author    = {Zhou, Peicheng and Fang, Shancheng and Jin, Chenhui and Pu, Bowei and Xie, Hongtao},
+    title     = {Towards Universal Open-Set Visual Font Recognition Via Augmented Synthetic Similarity},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Findings},
+    month     = {June},
+    year      = {2026},
+    pages     = {6799-6808}
+}
+```
+
+## 🙏 Acknowledgement
+
+We thank the open-source projects [ms-swift](https://github.com/modelscope/ms-swift), [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [LLaVA-OneVision](https://github.com/LLaVA-VL/LLaVA-NeXT), and [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) on top of which DesignVFR was built.

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+# Auto-generated by pack_for_huggingface.py
+"""One-line unpacker for the DesignVFR HuggingFace release.
+
+After ``huggingface-cli download <user>/DesignVFR --repo-type dataset``,
+run::
+
+    python unpack.py
+
+It will extract every ``*.tar`` / ``*.part-XXX.tar`` next to itself in-place
+so that the directory layout matches the paths inside the metadata
+jsonl/json (which are anchored on ``${DATASET_ROOT}``).
+"""
+from __future__ import annotations
+
+import sys
+import tarfile
+from pathlib import Path
+
+
+def main():
+    root = Path(__file__).resolve().parent
+    tars = sorted(root.rglob('*.tar'))
+    if not tars:
+        print('No .tar files found under', root)
+        return
+    print(f'Found {len(tars)} tar shard(s) under {root}')
+    for t in tars:
+        # Each tar is anchored on its parent directory's "image root" -- e.g.
+        # synthetic/train.part-001.tar  -> extract into synthetic/train/
+        # synthetic/ood_infer.tar       -> extract into synthetic/ood_infer/
+        stem = t.name.split('.', 1)[0]   # "train.part-001.tar" -> "train"
+        dest = t.parent / stem
+        dest.mkdir(parents=True, exist_ok=True)
+        print(f'  extracting {t.relative_to(root)}  ->  {dest.relative_to(root)}/')
+        with tarfile.open(t, 'r') as f:
+            f.extractall(dest)
+    print('Done. You can now `export DATASET_ROOT=' + str(root) + '`.')
+
+
+if __name__ == '__main__':
+    main()

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