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README.md
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Models of experiment: https://github.com/deepghs/tagger_embedding_aligner
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| Name | Tagger | Embedding Width | Tags Count | FLOPS | Params | EMB Cosine | EMB Norm | Pred Loss | Pred MSE |
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|:---------------------:|:-----------:|:-----------------:|:------------:|:---------:|:--------:|:------------:|:----------:|:-----------:|:----------:|
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| ViT_v3_mnum2_all | ViT_v3 | 768 | 10861 | 0.000398G | 0.40M | 1 | 0.1712 | 0.004306 | 2.116e-08 |
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Models of experiment: https://github.com/deepghs/tagger_embedding_aligner
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```python
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import numpy as np
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from imgutils.tagging import get_wd14_tags, convert_wd14_emb_to_prediction, denormalize_wd14_emb
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from test.testings import get_testfile
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embedding, (r, g, c) = get_wd14_tags(
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get_testfile('nian.png'),
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fmt=('embedding', ('rating', 'general', 'character')),
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)
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# normal tag results
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print('Expected result:')
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print(r)
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print(g)
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print(c)
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# normalize embedding
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embedding = embedding / np.linalg.norm(embedding)
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# bad tag results
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br, bg, bc = convert_wd14_emb_to_prediction(embedding)
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print('Bad results due to the embedding normalization:')
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print(br)
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print(bg)
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print(bc)
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# denormalize this embedding
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output = denormalize_wd14_emb(embedding)
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print(output.shape)
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# should be similar to r, g, c, approx 1e-3 error
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rating, general, character = convert_wd14_emb_to_prediction(output)
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print('De-normalized result:')
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print(rating)
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print(general)
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print(character)
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```
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| Name | Tagger | Embedding Width | Tags Count | FLOPS | Params | EMB Cosine | EMB Norm | Pred Loss | Pred MSE |
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|:---------------------:|:-----------:|:-----------------:|:------------:|:---------:|:--------:|:------------:|:----------:|:-----------:|:----------:|
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| ViT_v3_mnum2_all | ViT_v3 | 768 | 10861 | 0.000398G | 0.40M | 1 | 0.1712 | 0.004306 | 2.116e-08 |
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