---
license: apache-2.0
pipeline_tag: image-classification
tags:
- multi-label
- anime
- danbooru
---
Model ·
Demo ·
Quickstart ·
Quick comparisons
# 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:
```bash
# 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)
```python
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`](https://huggingface.co/pixai-labs/pixai-tagger-v0.9/blob/main/handler.py) under **Files** for a minimal script.
- **Demo UI**: our [Huggingface Space](https://huggingface.co/spaces/pixai-labs/pixai-tagger-demo) above or this [Huggingface Space from DeepGHS](https://huggingface.co/spaces/deepghs/pixai-tagger-v0.9-demo).
- `pip` + direct weights: **TBD** (planned for v1.0).
Also , this Tagger can be used via the [imgutils tool](https://dghs-imgutils.deepghs.org/main/api_doc/tagging/pixai.html).
------
## 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**
| Model | Coverage-F1 | Accuracy-F1 | Acc-Recall | Acc-Precision | Cov-Precision | Cov-Recall |
| -------------- | ----------- | ----------- | ---------- | ------------- | ------------- | ---------- |
| **PixAI v0.9** | **0.4910** | 0.4403 | 0.6654 | 0.3634 | 0.4350 | 0.6547 |
| WD-v3-EVA02 | 0.4155 | 0.4608 | 0.4465 | **0.5248** | 0.4580 | 0.4083 |
| WD-v3-SwinV2 | 0.3349 | 0.3909 | 0.3603 | 0.4821 | 0.3906 | 0.3171 |
| Camie-70k | 0.4877 | 0.4800 | 0.5743 | 0.4123 | 0.4288 | 0.5930 |
> 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’.
| Topic | PixAI Tagger v0.9 | WD v3 (EVA02 / SwinV2) | What it means in practice |
| --------------------- | ---------------------------------------- | ---------------------- | ------------------------------------------------------------ |
| **Data snapshot** | Danbooru to **2025-01** | Danbooru to 2024-02 | Better coverage of newer IPs |
| **Tag vocabulary** | ~**13.5k** tags | ~10.8k tags | More labels to catch long-tail |
| **Character F1** | **≈0.865** (@ 0.75 threshold) | ~0.61 (SwinV2 ref) | Stronger character recognition |
| **Default posture** | Recall-leaning (tune down for precision) | Often more balanced | Good for search/curation; more false positives; set your own thresholds |
| **Model size** | **~1.27 GB** checkpoint | Similar ballpark | Easy to host; endpoint-friendly |
| **Training strategy** | Head-only; encoder frozen (EVA02) | Depends on release | Faster iteration on data updates |
------
## 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
- **[Linso](https://huggingface.co/richard-guyunqi)** — primary contributor (training, data processing)
- **[narugo1992](https://huggingface.co/narugo1992)** — contributions
- **[AngelBottomless](https://huggingface.co/AngelBottomless)** (PixAI) — contributions
- **[trojblue](https://huggingface.co/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](https://huggingface.co/deepghs/pixai-tagger-v0.9-onnx), thanks!