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deepghs/ccip_onnx
ccip_onnx is a zero-shot image classification model from deepghs. Use it for the zero-shot image classification task on the model card, and read the license before you ship it in a product. The card lists the license as openrail.
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. Cluster2 repres…
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Updated Sep 9, 2024
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From the Hugging Face model README
| 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 |
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.