Image Classification on ImageNet-1K (val) (Accuracy and Rate)
82.17AccuracyJ4D
Evaluation results
| Method | Links | ||
|---|---|---|---|
| J4DPre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Accuracy Comparison2026.06 | 82.17 | 5.88 | |
| JPEGPre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Accuracy Comparison2026.06 | 82.14 | 10.1 | |
| AutoJPEGPre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Accuracy Comparison2026.06 | 82.12 | 7.21 | |
| GooglePre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Accuracy Comparison2026.06 | 82.04 | 6.56 | |
| J4DPre-Trained Model=Swin-T, Comparison Type=Same Accuracy Comparison2026.06 | 81.14 | 5.04 | |
| JPEGPre-Trained Model=Swin-T, Comparison Type=Same Accuracy Comparison2026.06 | 81.13 | 10.1 | |
| AutoJPEGPre-Trained Model=Swin-T, Comparison Type=Same Accuracy Comparison2026.06 | 81.1 | 8.47 | |
| GooglePre-Trained Model=Swin-T, Comparison Type=Same Accuracy Comparison2026.06 | 81.07 | 6.6 | |
| J4DPre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Rate Comparison2026.06 | 79.54 | 1.88 | |
| GooglePre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Rate Comparison2026.06 | 79.04 | 1.88 | |
| AutoJPEGPre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Rate Comparison2026.06 | 78.93 | 1.93 | |
| JPEGPre-Trained Model=ConvNeXt-tiny, Comparison Type=Same Rate Comparison2026.06 | 78.16 | 1.86 | |
| J4DPre-Trained Model=Swin-T, Comparison Type=Same Rate Comparison2026.06 | 77.82 | 1.96 | |
| GooglePre-Trained Model=Swin-T, Comparison Type=Same Rate Comparison2026.06 | 76.78 | 1.92 | |
| AutoJPEGPre-Trained Model=Swin-T, Comparison Type=Same Rate Comparison2026.06 | 76.17 | 2 | |
| JPEGPre-Trained Model=Swin-T, Comparison Type=Same Rate Comparison2026.06 | 75.61 | 1.99 | |
| J4DPre-Trained Model=MnasNet, Comparison Type=Same Accuracy Comparison2026.06 | 73.42 | 4.07 | |
| JPEGPre-Trained Model=MnasNet, Comparison Type=Same Accuracy Comparison2026.06 | 73.41 | 10.1 | |
| AutoJPEGPre-Trained Model=MnasNet, Comparison Type=Same Accuracy Comparison2026.06 | 73.39 | 7.78 | |
| GooglePre-Trained Model=MnasNet, Comparison Type=Same Accuracy Comparison2026.06 | 73.37 | 6.71 | |
| J4DPre-Trained Model=MnasNet, Comparison Type=Same Rate Comparison2026.06 | 71.98 | 1.92 | |
| GooglePre-Trained Model=MobileNetV2, Comparison Type=Same Accuracy Comparison2026.06 | 71.88 | 6.65 | |
| JPEGPre-Trained Model=MobileNetV2, Comparison Type=Same Accuracy Comparison2026.06 | 71.86 | 10.1 | |
| J4DPre-Trained Model=MobileNetV2, Comparison Type=Same Accuracy Comparison2026.06 | 71.86 | 5.15 | |
| AutoJPEGPre-Trained Model=MobileNetV2, Comparison Type=Same Accuracy Comparison2026.06 | 71.8 | 6.78 | |
| GooglePre-Trained Model=MnasNet, Comparison Type=Same Rate Comparison2026.06 | 71.65 | 1.96 | |
| JPEGPre-Trained Model=MnasNet, Comparison Type=Same Rate Comparison2026.06 | 71.2 | 1.93 | |
| J4DPre-Trained Model=MobileNetV2, Comparison Type=Same Rate Comparison2026.06 | 70.42 | 2 | |
| GooglePre-Trained Model=MobileNetV2, Comparison Type=Same Rate Comparison2026.06 | 69.81 | 1.95 | |
| JPEGPre-Trained Model=MobileNetV2, Comparison Type=Same Rate Comparison2026.06 | 69.54 | 2.02 | |
| AutoJPEGPre-Trained Model=MnasNet, Comparison Type=Same Rate Comparison2026.06 | 69.1 | 2 | |
| AutoJPEGPre-Trained Model=MobileNetV2, Comparison Type=Same Rate Comparison2026.06 | 67.47 | 1.93 |