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--- |
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datasets: |
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- deepghs/character_similarity |
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- deepghs/character_index |
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metrics: |
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- f1 |
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- adjust_random_score |
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language: |
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- en |
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- ja |
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- zh |
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pipeline_tag: zero-shot-image-classification |
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library_name: dghs-imgutils |
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tags: |
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- art |
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- anime |
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- character |
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license: openrail |
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--- |
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# CCIP |
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CCIP(Contrastive Anime Character Image Pre-Training) is a model to calculuate the visual similarity between anime characters in two images. (limited to images containing only a single anime character). More similar the characters between two images are, higher score it should have. |
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# Usage |
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Using CCIP with [imgutils](https://dghs-imgutils.deepghs.org/main/tutorials/installation/index.html) |
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Calculuate character similarity between images: |
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``` |
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from imgutils.metrics import ccip_batch_differences |
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ccip_batch_differences(['ccip/1.jpg', 'ccip/2.jpg', 'ccip/6.jpg', 'ccip/7.jpg']) |
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array([[6.5350548e-08, 1.6583106e-01, 4.2947042e-01, 4.0375218e-01], |
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[1.6583106e-01, 9.8025822e-08, 4.3715334e-01, 4.0748104e-01], |
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[4.2947042e-01, 4.3715334e-01, 3.2675274e-08, 3.9229470e-01], |
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[4.0375218e-01, 4.0748104e-01, 3.9229470e-01, 6.5350548e-08]], |
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dtype=float32) |
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``` |
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[More detailed instruction](https://dghs-imgutils.deepghs.org/main/api_doc/metrics/ccip.html) |
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# Performence |
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| Model | F1 Score | Precision | Recall | Threshold | Cluster_2 | Cluster_Free | |
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|:-----------------------------------:|:----------:|:-----------:|:--------:|:-----------:|:-----------:|:--------------:| |
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| ccip-caformer_b36-24 | 0.940925 | 0.938254 | 0.943612 | 0.213231 | 0.89508 | 0.957017 | |
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| ccip-caformer-24-randaug-pruned | 0.917211 | 0.933481 | 0.901499 | 0.178475 | 0.890366 | 0.922375 | |
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| ccip-v2-caformer_s36-10 | 0.906422 | 0.932779 | 0.881513 | 0.207757 | 0.874592 | 0.89241 | |
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| ccip-caformer-6-randaug-pruned_fp32 | 0.878403 | 0.893648 | 0.863669 | 0.195122 | 0.810176 | 0.897904 | |
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| ccip-caformer-5_fp32 | 0.864363 | 0.90155 | 0.830121 | 0.183973 | 0.792051 | 0.862289 | |
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| ccip-caformer-4_fp32 | 0.844967 | 0.870553 | 0.820842 | 0.18367 | 0.795565 | 0.868133 | |
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| ccip-caformer_query-12 | 0.823928 | 0.871122 | 0.781585 | 0.141308 | 0.787237 | 0.809426 | |
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| ccip-caformer-23_randaug_fp32 | 0.81625 | 0.854134 | 0.781585 | 0.136797 | 0.745697 | 0.8068 | |
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| ccip-caformer-2-randaug-pruned_fp32 | 0.78561 | 0.800148 | 0.771592 | 0.171053 | 0.686617 | 0.728195 | |
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| ccip-caformer-2_fp32 | 0.755125 | 0.790172 | 0.723055 | 0.141275 | 0.64977 | 0.718516 | |
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* The calculation of `F1 Score`, `Precision`, and `Recall` considers "the characters in both images are the same" as a positive case. `Threshold` is determined by finding the maximum value on the F1 Score curve. |
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* `Cluster_2` represents the approximate optimal clustering solution obtained by tuning the eps value in DBSCAN clustering algorithm with min_samples set to `2`, and evaluating the similarity between the obtained clusters and the true distribution using the `random_adjust_score`. |
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* `Cluster_Free` represents the approximate optimal solution obtained by tuning the `max_eps` and `min_samples` values in the OPTICS clustering algorithm, and evaluating the similarity between the obtained clusters and the true distribution using the `random_adjust_score`. |
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# Citation |
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```bibtex |
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@misc{CCIP, |
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title={Contrastive Anime Character Image Pre-Training}, |
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author={Ziyi Dong and narugo1992}, |
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year={2024}, |
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howpublished={\url{https://huggingface.co/deepghs/ccip}} |
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} |
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``` |
