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CCIP

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.

Usage

Using CCIP with imgutils

Calculuate character similarity between images:

from imgutils.metrics import ccip_batch_differences

ccip_batch_differences(['ccip/1.jpg', 'ccip/2.jpg', 'ccip/6.jpg', 'ccip/7.jpg'])
array([[6.5350548e-08, 1.6583106e-01, 4.2947042e-01, 4.0375218e-01],
       [1.6583106e-01, 9.8025822e-08, 4.3715334e-01, 4.0748104e-01],
       [4.2947042e-01, 4.3715334e-01, 3.2675274e-08, 3.9229470e-01],
       [4.0375218e-01, 4.0748104e-01, 3.9229470e-01, 6.5350548e-08]],
      dtype=float32)

More detailed instruction

Performence

Model F1 Score Precision Recall Threshold Cluster_2 Cluster_Free
ccip-caformer_b36-24 0.940925 0.938254 0.943612 0.213231 0.89508 0.957017
ccip-caformer-24-randaug-pruned 0.917211 0.933481 0.901499 0.178475 0.890366 0.922375
ccip-v2-caformer_s36-10 0.906422 0.932779 0.881513 0.207757 0.874592 0.89241
ccip-caformer-6-randaug-pruned_fp32 0.878403 0.893648 0.863669 0.195122 0.810176 0.897904
ccip-caformer-5_fp32 0.864363 0.90155 0.830121 0.183973 0.792051 0.862289
ccip-caformer-4_fp32 0.844967 0.870553 0.820842 0.18367 0.795565 0.868133
ccip-caformer_query-12 0.823928 0.871122 0.781585 0.141308 0.787237 0.809426
ccip-caformer-23_randaug_fp32 0.81625 0.854134 0.781585 0.136797 0.745697 0.8068
ccip-caformer-2-randaug-pruned_fp32 0.78561 0.800148 0.771592 0.171053 0.686617 0.728195
ccip-caformer-2_fp32 0.755125 0.790172 0.723055 0.141275 0.64977 0.718516
  • 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.
  • 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.
  • 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.

operations benchmark

Citation

@misc{CCIP,
  title={Contrastive Anime Character Image Pre-Training},
  author={Ziyi Dong and narugo1992},
  year={2024},
  howpublished={\url{https://huggingface.co/deepghs/ccip}}
}
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