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license: apache-2.0 |
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datasets: |
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- kakaobrain/coyo-700m |
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[[Paper]](https://arxiv.org/abs/2407.17331) [[GitHub]](https://github.com/deepglint/unicom) |
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This model is trained using the COYO700M dataset. The results below are from linear probe evaluations, demonstrating the model's performance on various benchmarks. |
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| Dataset | CLIP | MLCD | |
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|-----------|------|------| |
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| Food101 | 88.8 | <span style="color:red">90.2</span> | |
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| CIFAR10 | 95.1 | <span style="color:red">96.9</span> | |
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| CIFAR100 | 80.5 | <span style="color:red">86.8</span> | |
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| Birdsnap | 58.5 | <span style="color:red">72.1</span> | |
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| SUN397 | 76.6 | <span style="color:red">77.4</span> | |
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| Cars | 81.8 | <span style="color:red">93.5</span> | |
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| Aircraft | 52.0 | <span style="color:red">74.7</span> | |
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| VOC2007 | 87.7 | <span style="color:red">90.4</span> | |
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| DTD | 76.5 | <span style="color:red">83.5</span> | |
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| Pets | 90.0 | <span style="color:red">93.6</span> | |
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| Cal101 | 93.0 | <span style="color:red">97.7</span> | |
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| Flowers | 96.9 | <span style="color:red">98.8</span> | |
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| ImageNet | 76.1 | <span style="color:red">79.1</span> | |
