| --- |
| license: apache-2.0 |
| library_name: pytorch |
| tags: |
| - font-recognition |
| - siglip2 |
| - multi-task |
| base_model: google/siglip2-base-patch16-naflex |
| models: |
| - google/siglip2-base-patch16-naflex |
| datasets: |
| - issai/DataFontID |
| - issai/Wild1024 |
| --- |
| |
| # FontID — SigLIP2 NaFlex, four-head font analyzer |
|
|
| Predicts **font family (75)**, **language (11)**, **color (64, EGA index)**, and |
| **style (4)** from a text-image crop. Backbone: `google/siglip2-base-patch16-naflex` |
| (native aspect ratio, `max_num_patches=256`); pooled feature (d=768) from the |
| SigLIP2 attention-pooling head feeds four independent heads |
| (`Linear(768→512) → LayerNorm → GELU → Dropout(0.1) → Linear(512→n)`). |
|
|
| ## Results (DataFontID test) |
|
|
| | font | language | color | style | |
| |------|----------|-------|-------| |
| | 96.34 | 96.19 | 95.92 | 96.98 | |
|
|
| Wild1024 out-of-distribution: language 82.42, font-category 89.55. |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import sys, torch, json |
| from transformers import AutoProcessor |
| from PIL import Image |
| |
| path = snapshot_download("issai/FontID") |
| sys.path.insert(0, path) |
| from modeling_fontid import FontIDModel |
| |
| model = FontIDModel.from_pretrained(path).eval() |
| proc = AutoProcessor.from_pretrained("google/siglip2-base-patch16-naflex") |
| maps = json.load(open(f"{path}/class_mappings.json")) |
| |
| img = Image.open("crop.png").convert("RGB") |
| o = proc(images=img, max_num_patches=256, return_tensors="pt") |
| with torch.no_grad(): |
| out = model(o["pixel_values"], o["pixel_attention_mask"], o["spatial_shapes"]) |
| for head in ["font", "lang", "color", "style"]: |
| idx = int(out[head].argmax(-1)) |
| print(head, maps[head][str(idx)]) |
| ``` |
|
|
| ## Citation |
|
|
| Paper is coming soon. |
|
|
