---
title: wd14_tagger_embedding_denormalize
canonical_url: "https://www.modelscope.cn/models/deepghs/wd14_tagger_embedding_denormalize"
md_url: "https://www.modelscope.cn/models/deepghs/wd14_tagger_embedding_denormalize.md"
repository: deepghs/wd14_tagger_embedding_denormalize
last_updated: 2024-12-04
license: openrail
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
library_name:
  - onnx
  - pytorch
frameworks:
  - pytorch
downloads: 1261
stars: 1
tags:
  - art
  - anime
  - cosine_space
---

# wd14_tagger_embedding_denormalize

> wd14_tagger_embedding_denormalize - deepghs 在 ModelScope 开源的模型。Models of experiment: https://github.com/deepghs/taggerembeddingaligner

deepghs/wd14_tagger_embedding_denormalize 是 ModelScope 魔搭社区上的feature-extraction模型，采用 openrail 许可。

- **Repository**: deepghs/wd14_tagger_embedding_denormalize
- **License**: openrail
- **Tasks**: feature-extraction
- **Tags**: art, anime, cosine_space
- **Downloads**: 1261
- **Stars**: 1
- **Last updated**: 2024-12-04

Source: https://www.modelscope.cn/models/deepghs/wd14_tagger_embedding_denormalize

---

Models of experiment: https://github.com/deepghs/tagger_embedding_aligner

```python
import numpy as np

from imgutils.tagging import get_wd14_tags, convert_wd14_emb_to_prediction, denormalize_wd14_emb

embedding, (r, g, c) = get_wd14_tags(
    '/my/image.png',
    fmt=('embedding', ('rating', 'general', 'character')),
)
# normal tag results
print('Expected result:')
print(r)
print(g)
print(c)

# normalize embedding
embedding = embedding / np.linalg.norm(embedding)
# bad tag results
br, bg, bc = convert_wd14_emb_to_prediction(embedding)
print('Bad results due to the embedding normalization:')
print(br)
print(bg)
print(bc)

# denormalize this embedding
output = denormalize_wd14_emb(embedding)
print(output.shape)

# should be similar to r, g, c, approx 1e-3 error
rating, general, character = convert_wd14_emb_to_prediction(output)
print('De-normalized result:')
print(rating)
print(general)
print(character)
```

|         Name          |   Tagger    |  Embedding Width  |  Tags Count  |   FLOPS   |  Params  |  EMB Cosine  |  EMB Norm  |  Pred Loss  |  Pred MSE  |
|:---------------------:|:-----------:|:-----------------:|:------------:|:---------:|:--------:|:------------:|:----------:|:-----------:|:----------:|
|   ViT_v3_mnum2_all    |   ViT_v3    |        768        |    10861     | 0.000398G |  0.40M   |      1       |   0.1712   |  0.004306   | 2.116e-08  |
|   ViT_v3_mnum1_all    |   ViT_v3    |        768        |    10861     | 0.000709G |  0.71M   |      1       |   0.2246   |  0.004306   | 3.991e-08  |
| ConvNext_v3_mnum2_all | ConvNext_v3 |       1024        |    10861     | 0.000708G |  0.71M   |      1       |   0.1126   |  0.004531   | 2.061e-08  |
| ConvNext_v3_mnum1_all | ConvNext_v3 |       1024        |    10861     | 0.001260G |  1.26M   |      1       |   0.1473   |  0.004531   | 3.539e-08  |
|     ViT_mnum2_all     |     ViT     |        768        |     9083     | 0.000398G |  0.40M   |      1       |  0.08641   |  0.005199   | 3.797e-09  |
|     ViT_mnum1_all     |     ViT     |        768        |     9083     | 0.000709G |  0.71M   |      1       |   0.1724   |  0.005199   | 1.896e-08  |
|  ConvNext_mnum2_all   |  ConvNext   |       1024        |     9083     | 0.000708G |  0.71M   |      1       |  0.05776   |  0.005213   | 7.207e-09  |
|  ConvNext_mnum1_all   |  ConvNext   |       1024        |     9083     | 0.001260G |  1.26M   |      1       |  0.07134   |  0.005214   | 1.292e-08  |
|  ViT_Large_mnum2_all  |  ViT_Large  |       1024        |    10861     | 0.000708G |  0.71M   |      1       |   1.403    |  0.003966   | 1.617e-07  |
|  ViT_Large_mnum1_all  |  ViT_Large  |       1024        |    10861     | 0.001260G |  1.26M   |      1       |   1.643    |  0.003966   |  2.24e-07  |
|   SwinV2_mnum2_all    |   SwinV2    |       1024        |     9083     | 0.000708G |  0.71M   |      1       |   0.1257   |  0.004726   | 3.797e-08  |
|   SwinV2_mnum1_all    |   SwinV2    |       1024        |     9083     | 0.001260G |  1.26M   |      1       |   0.1497   |  0.004727   | 5.487e-08  |
| EVA02_Large_mnum2_all | EVA02_Large |       1024        |    10861     | 0.000708G |  0.71M   |      1       |   1.268    |  0.005948   | 5.466e-08  |
| EVA02_Large_mnum1_all | EVA02_Large |       1024        |    10861     | 0.001260G |  1.26M   |      1       |   1.713    |  0.005948   | 9.518e-08  |
| ConvNextV2_mnum2_all  | ConvNextV2  |       1024        |     9083     | 0.000708G |  0.71M   |      1       |  0.09014   |  0.004596   |  1.43e-08  |
| ConvNextV2_mnum1_all  | ConvNextV2  |       1024        |     9083     | 0.001260G |  1.26M   |      1       |   0.1216   |  0.004596   |  2.76e-08  |
|  SwinV2_v3_mnum2_all  |  SwinV2_v3  |       1024        |    10861     | 0.000708G |  0.71M   |      1       |   0.2129   |  0.004128   | 4.035e-08  |
|  SwinV2_v3_mnum1_all  |  SwinV2_v3  |       1024        |    10861     | 0.001260G |  1.26M   |      1       |   0.2784   |  0.004129   | 6.893e-08  |
|    MOAT_mnum2_all     |    MOAT     |       1024        |     9083     | 0.000708G |  0.71M   |      1       |   0.4662   |  0.004998   | 1.855e-08  |
|    MOAT_mnum1_all     |    MOAT     |       1024        |     9083     | 0.001260G |  1.26M   |      1       |   0.7849   |  0.004998   | 5.549e-08  |