Lottery Jackpots Exist in Pre-trained Models (Paper Link)
- Python >= 3.7.4
- Pytorch >= 1.6.1
- Torchvision >= 0.4.1
-
Download the pre-trained models from this link and place them in the
pre-train
folder. -
Select a configuration file in
configs
to reproduce the experiment results reported in the paper. For example, to find a lottery jackpot with 30 epochs for pruning 95% parameters of ResNet-32 on CIFAR-10, run:python cifar.py --config configs/resnet32_cifar10/90sparsity30epoch.yaml --gpus 0
To find a lottery jackpot with 30 epochs for pruning 90% parameters of ResNet-50 on ImageNet, run:
python imagenet.py --config configs/resnet50_imagenet/90sparsity30epoch.yaml --gpus 0
Note that the
data_path
in the yaml file should be changed to the data.
We provide configuration, training logs, and pruned models reported in the paper. They can be downloaded from the provided links in the following table:
Model | Dataset | Sparsity | Epoch | Top-1 Acc. | Link |
---|---|---|---|---|---|
VGGNet-19 | CIFAR-10 | 90% | 30(S) | 93.88% | link |
VGGNet-19 | CIFAR-10 | 90% | 160(S) | 93.94% | link |
VGGNet-19 | CIFAR-10 | 95% | 30(S) | 93.49% | link |
VGGNet-19 | CIFAR-10 | 95% | 160(S) | 93.74% | link |
VGGNet-19 | CIFAR-100 | 90% | 30(S) | 72.59% | link |
VGGNet-19 | CIFAR-100 | 90% | 160(S) | 74.61% | link |
VGGNet-19 | CIFAR-100 | 95% | 30(S) | 71.76% | link |
VGGNet-19 | CIFAR-100 | 95% | 160(S) | 73.35% | link |
ResNet-32 | CIFAR-10 | 90% | 30(S) | 93.70% | link |
ResNet-32 | CIFAR-10 | 90% | 160(S) | 94.39% | link |
ResNet-32 | CIFAR-10 | 95% | 30(S) | 92.90% | link |
ResNet-32 | CIFAR-10 | 95% | 160(S) | 93.41% | link |
ResNet-32 | CIFAR-100 | 90% | 30(S) | 72.22% | link |
ResNet-32 | CIFAR-100 | 90% | 160(S) | 73.43% | link |
ResNet-32 | CIFAR-100 | 95% | 30(S) | 69.38% | link |
ResNet-32 | CIFAR-100 | 95% | 160(S) | 70.31% | link |
ResNet-50 | ImageNet | 80% | 30(S) | 75.19% | link |
ResNet-50 | ImageNet | 90% | 30(S) | 72.43% | link |
To test our pruned models, download the pruned models and place them in the ckpt
folder.
-
Select a configuration file in
configs
to test the pruned models. For example, to evaluate a lottery jackpot for pruning ResNet-32 on CIFAR-10, run:python evaluate.py --config configs/resnet32_cifar10/evaluate.yaml --gpus 0
To evaluate a lottery jackpot for pruning ResNet-50 on ImageNet, run:
python evaluate.py --config configs/resnet50_imagenet/evaluate.yaml --gpus 0