https://github.com/skwzrd/36g-rain-tagger
Lightweight, accurate image tagger, API and Web UI.
https://github.com/skwzrd/36g-rain-tagger
ai-image-tagging image-analysis image-classification image-processing image-tagger image-tagging images local python python3 tagger tagging web-ui
Last synced: about 2 months ago
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Lightweight, accurate image tagger, API and Web UI.
- Host: GitHub
- URL: https://github.com/skwzrd/36g-rain-tagger
- Owner: skwzrd
- Created: 2024-12-06T05:08:14.000Z (6 months ago)
- Default Branch: master
- Last Pushed: 2025-02-07T04:38:13.000Z (4 months ago)
- Last Synced: 2025-02-07T05:26:32.708Z (4 months ago)
- Topics: ai-image-tagging, image-analysis, image-classification, image-processing, image-tagger, image-tagging, images, local, python, python3, tagger, tagging, web-ui
- Language: Python
- Homepage:
- Size: 4.21 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 36g Rain Tagger
36g Rain Tagger is an automated image tagging software.
With it, you can:
- Run a tagging cli utility that crawls a file system, mapping a set of 10.8k tags to each image.
- Host a web UI to search tagged images. Searching can be done via tags, or images.
36g Rain Tagger uses [timm](https://huggingface.co/docs/timm/index) and leverages the model [SmilingWolf/wd-swinv2-tagger-v3](https://huggingface.co/SmilingWolf/wd-swinv2-tagger-v3).
It is named after [36g](https://vocaloid.fandom.com/wiki/36g).
## Set Up
Select your pytorch configuration by going through the menu at [https://pytorch.org/get-started/locally/](https://pytorch.org/get-started/locally/).
If you're using a CPU on Ubuntu, it should be something like `python3.12 -m pip install torch --index-url https://download.pytorch.org/whl/cpu`. You don't need `torchvision` or `torchaudio`.
If you're using a GPU on Ubuntu, `python3.12 -m pip install torch` will suffice.
```bash
git clone https://github.com/skwzrd/36g-Rain-Tagger
cd 36g-Rain-Tagger
python3.12 -m venv venv
source venv/bin/activate
python3.12 -m pip install -r requirements.txt
cd src/
```
## Usage, The Tagger
An example,
```
python3.12 tagger.py --bsize 1 --gmin 0.2 --cmin 0.2 --exts jpg,jpeg,png,gif --printt --path /path/to/image.png
```
All the cli command options,
```
Image tagging utility for extracting and saving tags from images.
options:
-h, --help Show this help message and exit.
--path Path to an image file or a directory containing images. Can also accept a list of paths.
--gmin Minimum probability threshold for general tags. Range: [0.0, 1.0], where 1.0 means a very strong match. Default: 0.2
--cmin Minimum probability threshold for character tags. Range: [0.0, 1.0], where 1.0 means a very strong match. Default: 0.2
--exts Comma-separated list of valid image file extensions to process. Default: png,jpeg,jpg,gif
--nmax Maximum number of images to tag. Set to 0 to process all images found in the specified path. Default: 0
--bsize Batch size for processing images. For faster processing, use a batch size of 1. Default: 1
--db_name Name of the SQLite database file to save results. Default: ./image.db
--skip, --no-skip Skip images that already have tags saved in the database. Use --no-skip to reprocess them. Default: --no-skip
--idx, --no-idx Enable index-to-probability mappings. Required to save results. Use --no-idx to disable. Default: --idx
--save, --no-save Save results to the SQLite database. Use --no-save to skip saving. Default: --save
--printt, --no-printt Print results. Use --no-printt to disable printing. Default: --no-print
--cpu, --no-cpu Run on CPU instead of GPU. Use --no-cpu to use GPU. Default: --no-cpu
```
**Note:** The tagger will automatically download the image tagging model and save it to `~/.cache/huggingface/hub`.
## Web UI
```
cd src/
cp rename_to_configs.py configs.py
# set variables in 'configs.py'
python3.12 web.py
# optional
celery -A web.celery_app worker --loglevel=info --pool solo
# ^ can be run in the background using --detach
```
#### Desktop, Info Mode
#### Desktop, Gallery Mode
#### Desktop, Uploaded Image Search
#### Mobile, Gallery Mode
## Structures
The returned structures are determined by the `--idx` and `--no-idx` flags. With `--idx`, tag ids are gathered. With `--no-idx`, tags can be printed, but not saved.
```
Character: {}
Ratings: {'general': 0.944, 'sensitive': 0.053, 'questionable': 0.002, 'explicit': 0.0}
General tags: {'outdoors': 0.69, 'sky': 0.755, 'day': 0.68, 'cloud': 0.628, 'water': 0.767, 'blue_sky': 0.566, 'pokemon_(creature)': 0.558, 'no_humans': 0.851, 'ocean': 0.712, 'surfboard': 0.7}
```
## Performance
### Tagging
Benchmarked using [https://github.com/sky-cake/image_data_set](https://github.com/sky-cake/image_data_set)
From `tagger.py`,
```python
main(
path='/path/to/image_data_set',
gmin=0.2,
cmin=0.2,
valid_extensions='png,jpeg,jpg,gif',
bsize=1,
nmax=0,
db_name=make_path('image.db'),
skip=False,
idx=True,
save=True,
printt=False,
cpu=?,
)
```
| Device | Images | Total Time (s) | Time per Image (s) |
|----------------|:----------:|---------------:|-------------------:|
| 4060 TI 16GB GPU | 45 | 2.172 | 0.048 |
| 5700X x 8 CPU | 45 | 21.277 | 0.473 |
| i7 8665U x 8 CPU | 45 | 76.273 | 1.695 |
### Searching
0.1s - 0.4s results on hundreds of thousands of images.
`Searched 238,302 in 0.313s and found 25 results.`