PixAI Tagger v0.9

A practical anime multi-label tagger. Not trying to win benchmarks; trying to be useful.
High recall, updated character coverage, trained on a fresh Danbooru snapshot (2025-01).
We’ll keep shipping: v1.0 (with updated tags) is next.

TL;DR

• ~13.5k Danbooru-style tags (general, character, copyright)
• Headline: strong character performance; recall-leaning defaults
• Built for search, dataset curation, caption assistance, and text-to-image conditioning

What it is (in one breath)

pixai-tagger-v0.9 is a multi-label image classifier for anime images. It predicts Danbooru-style tags and aims to find more of the right stuff (recall) so you can filter later. We continued training the classification head of EVA02 (from WD v3) on a newer dataset, and used embedding-space MixUp to help calibration.

• Last trained: 2025-04
• Data snapshot: Danbooru IDs 1–8,600,750 (2025-01)
• Finetuned from: SmilingWolf/wd-eva02-large-tagger-v3 (encoder frozen)
• License (weights): Apache 2.0 (Note: Danbooru content has its own licenses.)

Why you might care

• Newer data. Catches more recent IPs/characters.
• Recall-first defaults. Good for search and curation; dial thresholds for precision.
• Character focus. We spent time here; it shows up in evals.
• Simple to run. Works as an endpoint or locally; small set of knobs.

Quickstart

Recommended defaults (balanced):

• top_k = 128
• threshold_general = 0.30
• threshold_character = 0.75

Coverage preset (recall-heavier): threshold_general = 0.10 (expect more false positives)

1) Inference Endpoint

Deploy as an HF Inference Endpoint and test with the following command:

# Replace with your own endpoint URL
curl "https://YOUR_ENDPOINT_URL.huggingface.cloud" \
  -X POST \
  -H "Accept: application/json" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": {"url": "https://your.cdn/image.jpg"},
    "parameters": {
      "top_k": 128,
      "threshold_general": 0.10,
      "threshold_character": 0.75
    }
  }'

2) Python (InferenceClient)

from huggingface_hub import InferenceClient

client = InferenceClient("https://YOUR_ENDPOINT_URL.huggingface.cloud")
out = client.post(json={
    "inputs": {"url": "https://your.cdn/image.jpg"},
    "parameters": {"top_k": 128, "threshold_general": 0.10, "threshold_character": 0.75}
})
# out: [{"tag": "1girl", "score": 0.97, "group": "general"}, {"tag": "mika_(blue_archive)", "score": 0.92, "group": "character"}, ...]

3) Local Deployment

• Minimal Script: See handler.py under Files for a minimal script.
• Demo UI: our Huggingface Space above or this Huggingface Space from DeepGHS.
• pip + direct weights: TBD (planned for v1.0).

Also , this Tagger can be used via the imgutils tool.

Training notes (short version)

• Source: Danbooru (IDs 1–8,600,750; snapshot 2025-01)
• Tag set: ~13,461 tags (≥600 occurrences); grouped as general/character/copyright
• Filtering: remove images with <10 general tags (WD v3 heuristic)
• Setup: EVA02 encoder frozen; classification head continued training
• Input: 448×448; standard Danbooru tag normalization
• Augment: MixUp in embedding space (α=200)
• Optim: Adam 1e-5, cycle schedule; batch 2048; full precision
• Compute: ~1 day on 1× 8×H100 node
• (Explored full-backbone training; deferred—head-only was more stable and faster for data iteration.)

Evaluation (what to expect)

Metric style: Fixed thresholds (above). Reported as micro-averaged unless noted.

• All-tags (13k) micro-F1: ~0.60 (recall-leaning)
• Character subset (4k) micro-F1: 0.865 @ t_char=0.75
• Reference: WD v3 SwinV2 character F1 ≈ 0.608 (same protocol)

Internal “accuracy/coverage” snapshot

Notes • Character uses t≈0.75; coverage often uses t≈0.10. • Keep micro vs macro consistent when updating numbers.

Note: Plots show internal candidate versions (v2.x). Current release is equivalent to pixai-tagger-v0.9 (ex-v2.4.1). Follow-up version is in progress.

Quick comparisons

A fast feel for where v0.9 sits. Numbers are from our protocol and may differ from others’.

Intended use

You can:

• Auto-tag anime images with Danbooru-style tags
• Build tag-search indices
• Assist caption generation (merge tags with NL captions)
• Feed tags into text-to-image pipelines (alone or alongside text)

Please don’t rely on it for:

• Legal/safety moderation or age verification
• Non-anime imagery (performance will drop)
• Fine-grained counting/attributes without human review

Limitations & risks

• NSFW & sensitive tags. The dataset contains them; outputs may too.
• Recall vs precision. Low thresholds increase false positives.
• Hallucinations. Number-sensitive or visually similar tags can be mispredicted.
• Representation bias. Mirrors Danbooru’s styles, tropes, and demographics.
• IP/character names. Can be wrong or incomplete; use allow/deny lists and co-occurrence rules.

Tuning tips

• Set different thresholds for general vs character tags.
• Consider allow/deny lists for your domain.
• Add simple co-occurrence rules to suppress contradictions.

Authors / Contributors

• (name withheld by request) — primary contributor (training, data processing)
• narugo1992 — contributions
• AngelBottomless (PixAI) — contributions
• trojblue (PixAI) — contributions
• The rest of the PixAI team — further development support and testing

We also appreciate the broader anime image generation community. Several ideas, discussions, and experiments from outside PixAI helped shape this release.

Maintenance

• We plan future releases with updated snapshots.
• v1.0 will include updated tags + packaging improvements.
• Changelog will live in the repo.

Other

• There is an ONNX version of this Tagger provided by DeepGHS, thanks!