1038lab / pixai-tagger

ℹ️ This is a safetensors + tag JSON version of the original model pixai-labs/pixai-tagger-v0.9 .
🔄 Converted by 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.

Original Model · Quickstart · Training Notes · Credits

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
    }
  }'