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---
datasets:
- deepghs/character_similarity
- deepghs/character_index
metrics:
- f1
- adjust_random_score
language:
- en
- ja
- zh
pipeline_tag: zero-shot-image-classification
library_name: dghs-imgutils
tags:
- art
- anime
- character
license: openrail
---
# 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](https://dghs-imgutils.deepghs.org/main/tutorials/installation/index.html)
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](https://dghs-imgutils.deepghs.org/main/api_doc/metrics/ccip.html)
# 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`.
# Citation
```bibtex
@misc{CCIP,
title={Contrastive Anime Character Image Pre-Training},
author={Ziyi Dong and narugo1992},
year={2024},
howpublished={\url{https://huggingface.co/deepghs/ccip}}
}
``` |