lodestones / taggerine

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Model's Last Updated: April 28 2026
image-classification

Introduction of taggerine

Model Details of taggerine

DINOv3 ViT-H/16+ Booru Tagger

A multi-label image tagger trained on e621 and Danbooru annotations, using a DINOv3 ViT-H/16+ backbone fine-tuned end-to-end with a single linear projection head.

Model Details
Property Value
Backbone facebook/dinov3-vith16plus-pretrain-lvd1689m
Architecture ViT-H/16+ · 32 layers · hidden dim 1280 · 20 heads · SwiGLU MLP · RoPE · 4 register tokens
Head Linear((1 + 4) × 1280 → 74 625) — CLS + 4 register tokens concatenated
Vocabulary 74 625 tags (min frequency ≥ 50 across training set)
Input resolution Any multiple of 16 px — trained at 512 px, generalises to higher resolutions
Input normalisation ImageNet mean/std [0.485, 0.456, 0.406] / [0.229, 0.224, 0.225]
Output Raw logits — apply sigmoid for per-tag probabilities
Parameters ~632 M (backbone) + ~480 M (head)
Training
Hyperparameter Value
Training data e621 + Danbooru (parquet)
Batch size 32
Learning rate 1e-6
Warmup steps 50
Loss BCEWithLogitsLoss with per-tag pos_weight = (neg/pos)^(1/T) , cap 100
Optimiser AdamW (β₁=0.9, β₂=0.999, wd=0.01)
Precision bfloat16 (backbone) / float32 (projection + loss)
Hardware 2× GPU, ThreadPoolExecutor + NCCL all-reduce

eval_viz

Usage
1. Install dependencies
pip install -r requirements.txt

Or manually:

pip install torch torchvision safetensors Pillow requests \
            python-multipart fastapi uvicorn jinja2 aiofiles
2. Download model files
huggingface-cli download lodestones/taggerine \
    tagger_proto.safetensors \
    tagger_vocab_with_categories_and_alias_updated.json \
    tagger_ui_server.py \
    inference_tagger_standalone.py \
    --local-dir .

Note: tagger_proto.safetensors is ~5.3 GB. Make sure you have enough disk space.

3. Download the tagger_ui/ templates folder

The server requires the tagger_ui/templates/ directory to be present alongside tagger_ui_server.py :

huggingface-cli download lodestones/taggerine \
    --include "tagger_ui/**" \
    --local-dir .
4. Run the Web UI
python tagger_ui_server.py \
    --checkpoint tagger_proto.safetensors \
    --vocab tagger_vocab_with_categories_and_alias_updated.json \
    --port 7860
# → open http://localhost:7860

CPU-only machine? Add --device cpu (inference will be slower):

python tagger_ui_server.py \
    --checkpoint tagger_proto.safetensors \
    --vocab tagger_vocab_with_categories_and_alias_updated.json \
    --device cpu \
    --port 7860
Standalone CLI inference (no server)
python inference_tagger_standalone.py \
    --checkpoint tagger_proto.safetensors \
    --vocab tagger_vocab_with_categories_and_alias_updated.json \
    --images photo.jpg \
    --topk 30
Files
File Description
tagger_proto.safetensors Model weights (bfloat16)
tagger_vocab_with_categories_and_alias_updated.json {"idx2tag": [...], "tag2category": {...}} — 74 625 tags with category metadata
tagger_vocab_with_categories.json Same without alias data
tagger_vocab.json Minimal vocab — {"idx2tag": [...]} only
inference_tagger_standalone.py Self-contained CLI inference script (no transformers dep)
tagger_ui_server.py FastAPI + Jinja2 web UI server
requirements.txt Python dependencies
Tag Vocabulary

Tags are sourced from e621 and Danbooru annotations and cover:

  • Subject — species, character count, gender ( solo , duo , anthro , 1girl , male , …)
  • Body — anatomy, fur/scale/skin markings, body parts
  • Action / pose — looking at viewer , sitting , …
  • Scene — background, lighting, setting
  • Style — digital art , hi res , sketch , watercolor , …
  • Rating — explicit content tags are included; filter as needed for your use case

Minimum tag frequency threshold: 50 occurrences across the combined dataset.

Limitations
  • Evaluated on booru-style illustrations and furry art; performance on photographic images or other art works to some extend.
  • The vocabulary reflects the biases of e621 and Danbooru annotation practices.
License

Apache 2.0

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More taggerine license Visit here:

https://choosealicense.com/licenses/apache-2.0

taggerine huggingface.co

taggerine huggingface.co is an AI model on huggingface.co that provides taggerine's model effect (), which can be used instantly with this lodestones taggerine model. huggingface.co supports a free trial of the taggerine model, and also provides paid use of the taggerine. Support call taggerine model through api, including Node.js, Python, http.

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lodestones taggerine online free url in huggingface.co:

https://huggingface.co/lodestones/taggerine

taggerine install

taggerine is an open source model from GitHub that offers a free installation service, and any user can find taggerine on GitHub to install. At the same time, huggingface.co provides the effect of taggerine install, users can directly use taggerine installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

taggerine install url in huggingface.co:

https://huggingface.co/lodestones/taggerine

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