Benchmarks and Checkpoints
August 14, 2022 ยท View on GitHub
Each zip file contains 4 types of files
- a checkpoint of the model, typically, named as
model_best.pth.tar - the md5 of the checkpoint
- a hyper-parameter json file, typically, named as
hparams_train.json tensorboardlog file, you can usetensorboardto visualize the log. It is in thevaldirectory within the zip file.
Hope you have fun with these checkpoints.
Any issues about checkpoints should be raised at
ImageNet
General training protocols: batch size 256, epochs 120, cos learning rate 0.1, AutoAugment/RandAugment, Label smoothing, mixup, random erasing.
| Methods | Top-1/Top-5 Acc | MParams/GFLOPs | Checkpoints |
|---|---|---|---|
| ResNet-50, 224px | 78.84 / 94.47 | 25.7 / 5.5 | resnet50_split1_imagenet_256_06 |
| SE-ResNet-50, 224px | 79.47 / 94.54 | 28.2 / 4.9 | se_resnet50_split1_imagenet_256_01 |
| ResNeXSt-50, 4x16d, 224px | 79.85 / 94.98 | 17.8 / 4.3 | resnexst50_4x16d_split1_imagenet_256_01 |
| ResNeXSt-50, 8x16d, 224px | 80.90 / 95.36 | 30.5 / 6.8 | resnexst50_8x16d_split1_imagenet_256_03 |
| ResNeXSt-50, 4x32d, 224px | 81.10 / 95.49 | 37.1 / 8.3 | resnexst50_4x32d_split1_imagenet_256_05 |
| ResNet-110, 224px | 80.16 / 94.54 | 44.8 / 9.2 | resnet101_split1_imagenet_256_01 |
| WRN-50-2, 224px | 80.66 / 95.16 | 68.9 / 12.8 | wide_resnet50_2_split1_imagenet_256_01 |
| WRN-50-2, S=2, 224px | 79.64 / 94.82 | 51.4 / 10.9 | wide_resnet50_2_split2_imagenet_256_02 |
| WRN-50-3, 224px | 80.74 / 95.40 | 135.0 / 23.8 | wide_resnet50_3_split1_imagenet_256_01 |
| WRN-50-3, S=2, 224px | 81.42 / 95.62 | 138.0 / 25.6 | wide_resnet50_3_split2_imagenet_256_02 |
| ResNeXt-101, 64x4d, 224px | 81.57 / 95.73 | 83.6 / 16.9 | resnext101_64x4d_split1_imagenet_256_01 |
| ResNeXt-101, 64x4d, S=2, 224px | 82.13 / 95.98 | 88.6 / 18.8 | resnext101_64x4d_split2_imagenet_256_02 |
| EfficientNet-B7, 320px | 81.83 / 95.78 | 66.7 / 10.6 | efficientnetb7_split1_imagenet_128_03 |
| EfficientNet-B7, S=2, 320px | 82.74 / 96.30 | 68.2 / 10.5 | efficientnetb7_split2_imagenet_128_02 |
| SE-ResNeXt-101, 64x4d, S=2, 416px, 120 epochs | 83.34 / 96.61 | 98.0 / 61.1 | se_resnext101_64x4d_split2_imagenet_128_02 |
| SE-ResNeXt-101, 64x4d, S=2, 320px, 350 epochs | 83.60 / 96.69 | 98.0 / 38.2 | se_resnext101_64x4d_B_split2_imagenet_128_05 |
CIFAR-100
| Methods | Top-1 Acc | MParams/GFLOPs | Checkpoints |
|---|---|---|---|
| WRN-28-10 | 84.50 | 36.5 / 5.25 | wide_resnet28_10_split1_cifar100_128_01_acc84.5 |
| WRN-28-10, S=2 | 85.52 | 35.8 / 5.16 | wide_resnet28_10_split2_cifar100_128_02_acc85.52 |
| WRN-28-10, S=4 | 85.74 | 36.7 / 5.28 | wide_resnet28_10_split4_cifar100_128_03_acc85.74 |
| WRN-40-10 | 83.98 | 55.9 / 8.08 | wide_resnet40_10_split1_cifar100_128_06_acc83.98 |
| WRN-40-10, S=2 | 85.91 | 54.8 / 7.94 | wide_resnet40_10_split2_cifar100_128_05_acc85.91 |
| WRN-40-10, S=4 | 86.90 | 56.0 / 8.12 | wide_resnet40_10_split4_cifar100_128_04_acc86.90 |
| DenseNet-BC-190 | 85.90 | 25.8 / 9.39 | densenet190_split1_cifar100_64_01_acc85.90 |
| DenseNet-BC-190, S=2 | 87.36 | 25.5 / 9.24 | densenet190_split2_cifar100_128_02_acc87.36 |
| DenseNet-BC-190, S=4 | 87.44 | 26.3 / 9.48 | densenet190_split4_cifar100_64_03_acc87.44 |
| PyramidNet-272 | 88.98 | 26.8 / 4.55 | pyramidnet272_split1_cifar100_128_01_88.98 |
| PyramidNet-272, S=2 | 89.25 | 28.9 / 5.24 | pyramidnet272_split2_cifar100_128_06_acc89.25 |
| PyramidNet-272, S=4 | 89.46 | 32.8 / 6.33 | pyramidnet272_split4_cifar100_128_07_acc89.46 |
CIFAR-10
| Methods | Top-1 Acc | MParams/GFLOPs | Checkpoints |
|---|---|---|---|
| WRN-28-10 | 97.59 | 36.5 / 5.25 | wide_resnet28_10_split1_cifar10_128_08_acc97.59 |
| WRN-28-10, S=2 | 98.19 | 35.8 / 5.16 | wide_resnet28_10_split2_cifar10_128_07_acc98.19 |
| WRN-28-10, S=4 | 98.32 | 36.5 / 5.28 | wide_resnet28_10_split4_cifar10_128_24_acc98.32 |
| WRN-40-10 | 97.81 | 55.8 / 8.08 | wide_resnet40_10_split1_cifar10_128_04_acc97.81 |
| WRN-40-10, S=4 | 98.38 | 55.9 / 8.12 | wide_resnet40_10_split4_cifar10_128_05_acc98.38 |
| Shake-Shake 26 2x96d | 98.00 | 26.2 / 3.78 | shake_resnet26_2x96d_split1_cifar10_128_07_acc98.00 |
| Shake-Shake 26 2x96d, S=2 | 98.25 | 23.3 / 3.38 | shake_resnet26_2x96d_split2_cifar10_128_12 |
| Shake-Shake 26 2x96d, S=4 | 98.31 | 26.3 / 3.81 | shake_resnet26_2x96d_split4_cifar10_128_09 |
| PyramidNet-272 | 98.67 | 26.2 / 4.55 | pyramidnet272_split1_cifar10_128_01_acc98.67 |
| PyramidNet-272, S=4 | 98.71 | 32.6 / 6.33 | pyramidnet272_split4_cifar10_128_05_acc98.71 |
COCO val2017 (SSD300)
| Backbones | mAP | MParams/GFLOPs | Checkpoints |
|---|---|---|---|
| WRN-50-2 | 30.3 | 39.3 / 48.1 | ssd300_coco_ssdv2_02 |
| WRN-50-2, S=2 | 29.9 | 31.7 / 45.3 | ssd300_coco_ssdv2_06 |
| WRN-50-3 | 30.7 | 64.1 / 86.8 | ssd300_coco_ssdv2_07 |
| WRN-50-3, S=2 | 31.6 | 64.3 / 96.3 | ssd300_coco_ssdv2_09 |
| ResNeXt-101, 64x4d | 32.6 | 68.9 / 90.1 | ssd300_coco_ssdv2_14 |
| ResNeXt-101, 64x4d, S=2 | 34.1 | 69.8 / 100.5 | ssd300_coco_ssdv2_15 |