wdv3-timm-bulk

use timm to run the WD Tagger V3 models.

How To Use

1. clone the repository and enter the directory:

git clone https://github.com/nawka12/wdv3-timm-bulk
cd wd3-timm-bulk

2. Create a virtual environment and install the Python requirements.

If you're using Linux and Python 3.10.x, you can use the provided script:

bash setup.sh

Or if you're on Windows or running newer version of Python (eg. Python 3.11.x), you can do the following:

# Create virtual environment
python -m venv venv
# Activate it
source venv/bin/activate # or venv\Scripts\activate for windows
# Upgrade pip/setuptools/wheel
python -m pip install -U pip setuptools wheel
# At this point, optionally you can install PyTorch manually (e.g. if you are not using an nVidia GPU)
python -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu # or https://download.pytorch.org/whl/cu124 for utilizing GPU
# Install requirements
python -m pip install -r requirements.txt

3. Run the script, picking one of the 3 models to use:

python wdv3_timm.py <swinv2|convnext|vit|vit-large|eva02> path/to/dir

Example output from python wdv3_timm.py eva02 dataset:

Using cuda
Loading model 'eva02' from 'SmilingWolf/wd-eva02-large-tagger-v3'...
Loading tag list...
Creating data transform...
100%|██████████████████████████████████████████████████████████████████████████| 1.05k/1.05k [00:58<00:00, 17.9image/s]
Done!

The script will create a new file [imagename].txt containing the tags.