Instructions to use SmilingWolf/wd-vit-tagger-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use SmilingWolf/wd-vit-tagger-v3 with timm:
import timm model = timm.create_model("hf-hub:SmilingWolf/wd-vit-tagger-v3", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download model.msgpack from SmilingWolf/wd-vit-tagger-v3: direct link, hf CLI and curl.
- Browser
- Download file 378 MB
-
https://huggingface.co/SmilingWolf/wd-vit-tagger-v3/resolve/main/model.msgpack
- Command line
-
hf download hf://SmilingWolf/wd-vit-tagger-v3/model.msgpack
-
curl -L -o model.msgpack https://huggingface.co/SmilingWolf/wd-vit-tagger-v3/resolve/main/model.msgpack
378 MB
- Xet hash:
- 1e3c8b6c53708ebea948e1b8f9e01f92dcf73670e3f86bfc316f1b9c5d6c4a95
- Size of remote file:
- 378 MB
- SHA256:
- a4b68183d5db1191ed4dbe0bfa816483454e55612b4e3a0c76e06229734c4ed6
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