Instructions to use animetimm/swinv2_base_window8_256.dbv4a-fullxx-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use animetimm/swinv2_base_window8_256.dbv4a-fullxx-cls with timm:
import timm model = timm.create_model("hf_hub:animetimm/swinv2_base_window8_256.dbv4a-fullxx-cls", pretrained=True) - Transformers
How to use animetimm/swinv2_base_window8_256.dbv4a-fullxx-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="animetimm/swinv2_base_window8_256.dbv4a-fullxx-cls") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("animetimm/swinv2_base_window8_256.dbv4a-fullxx-cls", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Anime Classifier swinv2_base_window8_256.dbv4a-fullxx-cls
Model Details
- Model Type: Single-Label Image classification / feature backbone
- Model Stats:
- Params: 96.6M
- FLOPs / MACs: 121.6G / 60.7G
- Image size: train = 448 x 448, test = 448 x 448
- Dataset: deepghs/danbooru-wdtagger-v4a-w640-ws-fullxx-cls
- Labels Count: 9453
Results
| # | Top-1 | Top-5 | Macro (F1/P/R) | Micro (F1/P/R) |
|---|---|---|---|---|
| Validation | 90.49% | 95.44% | 0.881 / 0.901 / 0.885 | 0.905 / 0.905 / 0.905 |
| Test | 90.36% | 95.34% | 0.880 / 0.901 / 0.884 | 0.904 / 0.904 / 0.904 |
You can find label list in selected_tags.csv.
- Downloads last month
- -