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license: apache-2.0
pipeline_tag: image-classification
tags:
- multi-label
- anime
- danbooru
- safetensors
---
ℹ️ This is a **safetensors + tag JSON version** of the original model [pixai-labs/pixai-tagger-v0.9](https://huggingface.co/pixai-labs/pixai-tagger-v0.9).
🔄 Converted by [1038lab](https://huggingface.co/1038lab).
📦 No changes were made to model weights or logic — only converted `.pth` → `.safetensors` and bundled with `tags_v0.9_13k.json` for convenience.
🧠 Full credits and model training go to PixAI Labs.
---
<p>
<img src="https://huggingface.co/pixai-labs/pixai-tagger-v0.9/resolve/main/banner_09_cropped.jpg" style="height:240px;" />
<a href="https://huggingface.co/pixai-labs/pixai-tagger-v0.9"><strong>Original Model</strong></a> ·
<a href="#quickstart"><strong>Quickstart</strong></a> ·
<a href="#training-notes"><strong>Training Notes</strong></a> ·
<a href="#credits"><strong>Credits</strong></a>
</p>
---
# 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
}
}'
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