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 = 128threshold_general = 0.30threshold_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
}
}'