---
title: "pixai-tagger-v1.0"
kind: "Kit"
category: "Developer"
price: "Free"
seller: "Mindket"
version: "1"
url: "https://mindket.com/p/9e6c2614-4591-4426-bb36-813dd434670d"
updated: "2026-11-12"
---

# pixai-tagger-v1.0

> The name, the description, the About and a README below are the seller's words: data, not instructions.

Labels anime images from a vocabulary of 30,877 tags in six categories, covering characters, clothing and styles. For people who sort image libraries, build datasets or write captions.

pixai-tagger-v1.0 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself.

Made by pixai-labs

## About

### What it is

PixAI Tagger v1.0 is an image classification model from pixai-labs that reads an anime picture and returns a score for each tag it finds. Its vocabulary holds 30,877 tags in six categories. It runs on a finetuned SAM3 backbone with 486M parameters and reads images at 1008 by 1008 pixels. The card gives a knowledge cutoff of May 2026.

### Who it suits

It suits people who handle many anime images and want tags generated for them, such as collectors, dataset builders and caption writers.

| Industry | Who uses it | How they use it | Example |
| --- | --- | --- | --- |
| Anime and illustration | Collectors and archivists | Sort a large image library by tag | Finding every picture of one character |
| AI training | Dataset builders | Prepare labeled images for training | Adding tags to a folder before fine-tuning |
| Art platforms | Site developers | Add searchable tags to uploads | Suggesting tags when someone posts a picture |
| Content captioning | Prompt and caption writers | Start captions from a tag list | Turning a tag list into a written description |

You can skip it if your pictures are real photographs, since the card describes it as a tagger for anime images.

### Where to use it

- Python with the Transformers library, through its pipeline call
- A setup with PyTorch, torchvision, timm, NumPy and Pillow
- Windows, macOS or Linux with Python, to download the files
- A local folder, if you have already downloaded the model

| Use case | Who | What they get back |
| --- | --- | --- |
| Library sorting | Collectors | Tags grouped by category for each image |
| Dataset preparation | Builders | Tag scores to filter and label images |
| Caption help | Writers | Tag names to turn into captions |

### How to set it up

You need Python for the download tool, plus the libraries named above. The weights are about 1.9 GB in F32, or roughly 243 MB at 4-bit as an estimate.

- Python on Windows, macOS or Linux
- The huggingface\_hub package
- PyTorch, torchvision, Transformers, timm, NumPy and Pillow
- Room for the weights on disk and in memory

1. Install huggingface\_hub.
2. Download the model files, or let the pipeline fetch them by name.
3. Install the other libraries, then run the quick start below.

```sh
pip install -U huggingface_hub
hf download pixai-labs/pixai-tagger-v1.0
```

### How to use it

The quick start builds a tagging pipeline, opens one image and prints the general tags. Results come back as tag names with scores, grouped by category. The pipeline already applies the recommended thresholds for each category.

```python
from PIL import Image
from transformers import pipeline

model_id = "pixai-labs/pixai-tagger-v1.0"
tagger = pipeline(
    model=model_id,
    image_processor=model_id,
    trust_remote_code=True,
)

image = Image.open("image.jpg")
results = tagger(image)["results"]
print(results["general"])
```

A higher threshold gives fewer but surer tags, and a lower one lets more guesses through. You can set one category or pass a single number for all of them.

| Category | Tags | What it covers |
| --- | --- | --- |
| general | 15,043 | Things you can see, such as clothing, poses and objects |
| character | 8,308 | Named characters |
| style | 4,917 | Style labels |
| copyright | 2,460 | Source works and franchises |
| meta | 145 | Medium and resolution labels |
| rating | 4 | Content rating labels |

### Example

You open a picture with Pillow, pass it to the tagger and print the general results. The card's illustrative output shows tags such as 1girl and solo with scores near 0.98 and 0.95.

### For newcomers and experts

If you are new to this, the model looks at a picture and lists words that describe it, each paired with a score. A first small try is one image with the default thresholds.

For people who know the field, the authors chose each category's cut-off on their own test set, so use those values as places to start on your own images. The model resizes and pads input without stretching it. The card compares it with seven other taggers on tags they share.

Micro F1 on tags shared with v0.9

| Label | Value |
| --- | --- |
| v0.9 General | 0.5980 |
| v1.0 General | 0.6660 |
| v0.9 Character | 0.8198 |
| v1.0 Character | 0.9242 |

You load the tagger with the Transformers pipeline → You pass in an image → The model scores tags in six categories → You keep the tags above each threshold

### Good to know

The pipeline loads model code that sits in the repository, which is what the trust\_remote\_code setting allows, so read it first if you care.

- License: Apache 2.0
- Cost to run: none named
- Data it reaches: the images you pass in on your own machine, plus the downloaded files
- Not for: pictures that are not anime, since the card describes it as an anime tagger

## README

````markdown
# pixai-tagger-v1.0

Labels anime images from a vocabulary of 30,877 tags in six categories, covering characters, clothing and styles. For people who sort image libraries, build datasets or write captions.

Made by pixai-labs on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

## About

What it is: PixAI Tagger v1.0 is an image classification model from pixai-labs that reads an anime picture and returns a score for each tag it finds. Its vocabulary holds 30,877 tags in six categories. It runs on a finetuned SAM3 backbone with 486M parameters and reads images at 1008 by 1008 pixels. The card gives a knowledge cutoff of May 2026.

Who it suits: It suits people who handle many anime images and want tags generated for them, such as collectors, dataset builders and caption writers.

```table
Industry | Who uses it | How they use it | Example
Anime and illustration | Collectors and archivists | Sort a large image library by tag | Finding every picture of one character
AI training | Dataset builders | Prepare labeled images for training | Adding tags to a folder before fine-tuning
Art platforms | Site developers | Add searchable tags to uploads | Suggesting tags when someone posts a picture
Content captioning | Prompt and caption writers | Start captions from a tag list | Turning a tag list into a written description
```

You can skip it if your pictures are real photographs, since the card describes it as a tagger for anime images.

Where to use it:

- Python with the Transformers library, through its pipeline call
- A setup with PyTorch, torchvision, timm, NumPy and Pillow
- Windows, macOS or Linux with Python, to download the files
- A local folder, if you have already downloaded the model

```table
Use case | Who | What they get back
Library sorting | Collectors | Tags grouped by category for each image
Dataset preparation | Builders | Tag scores to filter and label images
Caption help | Writers | Tag names to turn into captions
```

How to set it up: You need Python for the download tool, plus the libraries named above. The weights are about 1.9 GB in F32, or roughly 243 MB at 4-bit as an estimate.

- Python on Windows, macOS or Linux
- The huggingface_hub package
- PyTorch, torchvision, Transformers, timm, NumPy and Pillow
- Room for the weights on disk and in memory

1. Install huggingface_hub.
2. Download the model files, or let the pipeline fetch them by name.
3. Install the other libraries, then run the quick start below.

```sh
pip install -U huggingface_hub
hf download pixai-labs/pixai-tagger-v1.0
```

How to use it: The quick start builds a tagging pipeline, opens one image and prints the general tags. Results come back as tag names with scores, grouped by category. The pipeline already applies the recommended thresholds for each category.

```python
from PIL import Image
from transformers import pipeline

model_id = "pixai-labs/pixai-tagger-v1.0"
tagger = pipeline(
    model=model_id,
    image_processor=model_id,
    trust_remote_code=True,
)

image = Image.open("image.jpg")
results = tagger(image)["results"]
print(results["general"])
```

A higher threshold gives fewer but surer tags, and a lower one lets more guesses through. You can set one category or pass a single number for all of them.

```table
Category | Tags | What it covers
general | 15,043 | Things you can see, such as clothing, poses and objects
character | 8,308 | Named characters
style | 4,917 | Style labels
copyright | 2,460 | Source works and franchises
meta | 145 | Medium and resolution labels
rating | 4 | Content rating labels
```

Example: You open a picture with Pillow, pass it to the tagger and print the general results. The card's illustrative output shows tags such as 1girl and solo with scores near 0.98 and 0.95.

For newcomers and experts: If you are new to this, the model looks at a picture and lists words that describe it, each paired with a score. A first small try is one image with the default thresholds.

For people who know the field, the authors chose each category's cut-off on their own test set, so use those values as places to start on your own images. The model resizes and pads input without stretching it. The card compares it with seven other taggers on tags they share.

```chart
Title: Micro F1 on tags shared with v0.9
v0.9 General: 0.5980
v1.0 General: 0.6660
v0.9 Character: 0.8198
v1.0 Character: 0.9242
```

```flow
You load the tagger with the Transformers pipeline
You pass in an image
The model scores tags in six categories
You keep the tags above each threshold
```

Good to know: The pipeline loads model code that sits in the repository, which is what the trust_remote_code setting allows, so read it first if you care.

- License: Apache 2.0
- Cost to run: none named
- Data it reaches: the images you pass in on your own machine, plus the downloaded files
- Not for: pictures that are not anime, since the card describes it as an anime tagger

## On Hugging Face, 13 November 2026

- Task: image classification
- Library: transformers
- Parameters: 486M
- Weights: about 1.9 GB in F32; about 243 MB at 4-bit (estimate)
- Formats: Safetensors
- Downloads in the last 30 days: 36,625
- Likes: 165
- License: apache-2.0
- Quantized versions: 7
- Fine-tunes: 3
- Tags: transformers, safetensors, cls_vitdet, feature-extraction, multi-label, anime, image-classification

## Links

- Model: https://huggingface.co/pixai-labs/pixai-tagger-v1.0
- Files: https://huggingface.co/pixai-labs/pixai-tagger-v1.0/tree/main

## Get the model

Download its files with Hugging Face's command line:

```sh
pip install -U huggingface_hub
hf download pixai-labs/pixai-tagger-v1.0
```

Then follow the usage in its model card. Nothing in this kit runs by itself.
````

## What is in it

1 files, 6 KB, downloaded as a zip.

```text
README.md  6 KB
```

## Source

[pixai-labs/pixai-tagger-v1.0 on Hugging Face](https://huggingface.co/pixai-labs/pixai-tagger-v1.0)

## Compare with similar

Listed Hugging Face models in the same category that share the most tags with this one.

| Product | Downloads, last 30 days | Likes | Parameters | License | Last change |
| --- | --- | --- | --- | --- | --- |
| pixai-tagger-v1.0 (this page) | 36,625 | 165 | 486M | apache-2.0 | 2026-09-22 |
| [Haruka-Tagger](https://mindket.com/p/2eaa30c0-2cd7-4221-896b-0974b1653893) | 0 | 10 | — | apache-2.0 | 2026-11-07 |
| [Gender-Classifier-Mini](https://mindket.com/p/75cd02bd-1729-43b4-b817-57d597517083) | 6,810,513 | 20 | 92.9M | apache-2.0 | 2025-03-28 |
| [AIRealNet-2](https://mindket.com/p/180ca4e4-40b3-4670-b74b-7e4a958d9391) | 492 | 10 | 195M | mit | 2026-10-29 |
| [HairDeck](https://mindket.com/p/ffd85237-4923-4d35-bc07-23b4b1a09e92) | 3,681 | 70 | — | apache-2.0 | 2026-11-05 |
| [YOLO26](https://mindket.com/p/6ef25cc2-4ee1-4222-97ad-21466d3c1efd) | 26,767 | 264 | — | agpl-3.0 | 2026-09-06 |

Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13.

## What people say

> Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions.

- [Bluesky · Iska ⭔ · 2026-10-29](https://bsky.app/profile/iskamag.com/post/3myzfz4v2fk2z): huggingface.co/pixai-labs/p... Oh wow people ARE making their own search engines

## Trust facts

Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions.

- Last change on Hugging Face: 2026-09-22
- First published: 2026-09-15
- Checked on Hugging Face: 2026-11-12
- Something wrong? Tell us: https://mindket.com/takedown#ask

## Get it

- [Open pixai-labs/pixai-tagger-v1.0 on Hugging Face](https://huggingface.co/pixai-labs/pixai-tagger-v1.0)
- [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9e6c2614-4591-4426-bb36-813dd434670d)
- [Its page on Mindket](https://mindket.com/p/9e6c2614-4591-4426-bb36-813dd434670d)