Image Classification on ImageNet-1k 1 (val)
75.72Top-1 AccFOAM
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| FOAMf=20, τ=0.75, ϵ0=10−9, ϵmax=3 × 10−72026.06 | 75.72 | — | — | |
| stale Shampoof=20, τ=N/A, ϵ0=10−9, ϵmax=N/A2026.06 | 75.13 | — | — | |
| SOAPf=20, τ=N/A, ϵ0=10−9, ϵmax=N/A2026.06 | 75.1 | — | — | |
| DR-Shampoof=20, τ=0.75, ϵ0=10−9, ϵmax=N/A2026.06 | 73.56 | — | — | |
| AdamWf=N/A, τ=N/A, ϵ0=10−9, ϵmax=N/A2026.06 | 72.39 | — | — | |
| PRACTISEBackbone=ResNet-34, Training samples=1000, Pruning strategy=dropping blocks2022.02 | 71.9 | 90.6 | 32.5 | |
| PRACTISEBackbone=ResNet-34, Training samples=500, Pruning strategy=dropping blocks2022.02 | 71.8 | 90.5 | 32.5 | |
| PRACTISEBackbone=ResNet-34, Training samples=100, Pruning strategy=dropping blocks2022.02 | 70.4 | 89.7 | 32.5 | |
| PRACTISEBackbone=ResNet-34, Training samples=50, Pruning strategy=dropping blocks2022.02 | 68 | 88.2 | 32.5 | |
| BP (block)Backbone=ResNet-34, Training samples=1000, Pruning strategy=block pruning2022.02 | 66.8 | 87.5 | 32.5 | |
| MiRBackbone=ResNet-34, Training samples=1000, Pruning strategy=filter pruning2022.02 | 66.6 | 87.7 | 33.8 | |
| MiRBackbone=ResNet-34, Training samples=500, Pruning strategy=filter pruning2022.02 | 65.4 | 87 | 33.8 | |
| BP (block)Backbone=ResNet-34, Training samples=500, Pruning strategy=block pruning2022.02 | 65 | 86.5 | 32.5 | |
| MiRBackbone=ResNet-34, Training samples=100, Pruning strategy=filter pruning2022.02 | 62.1 | 84.8 | 33.8 | |
| BP (block)Backbone=ResNet-34, Training samples=100, Pruning strategy=block pruning2022.02 | 61.6 | 84.3 | 32.5 | |
| BP (block)Backbone=ResNet-34, Training samples=50, Pruning strategy=block pruning2022.02 | 60.6 | 83.5 | 32.5 | |
| MiRBackbone=ResNet-34, Training samples=50, Pruning strategy=filter pruning2022.02 | 59.9 | 83.2 | 33.8 | |
| BP (filter)Backbone=ResNet-34, Training samples=1000, Pruning strategy=filter pruning2022.02 | 51.2 | 76.5 | 33.8 | |
| KDBackbone=ResNet-34, Training samples=1000, Pruning strategy=filter pruning2022.02 | 50.5 | 75.9 | 33.8 | |
| KDBackbone=ResNet-34, Training samples=500, Pruning strategy=filter pruning2022.02 | 45.7 | 72.2 | 33.8 | |
| FSKDBackbone=ResNet-34, Training samples=1000, Pruning strategy=filter pruning2022.02 | 44.9 | 70.5 | 33.8 | |
| BP (filter)Backbone=ResNet-34, Training samples=500, Pruning strategy=filter pruning2022.02 | 42.9 | 70.5 | 33.8 | |
| FSKDBackbone=ResNet-34, Training samples=500, Pruning strategy=filter pruning2022.02 | 42.8 | 69.1 | 33.8 | |
| FSKDBackbone=ResNet-34, Training samples=100, Pruning strategy=filter pruning2022.02 | 36.6 | 63.1 | 33.8 | |
| KDBackbone=ResNet-34, Training samples=100, Pruning strategy=filter pruning2022.02 | 33.1 | 61 | 33.8 | |
| FSKDBackbone=ResNet-34, Training samples=50, Pruning strategy=filter pruning2022.02 | 31.1 | 56.5 | 33.8 | |
| KDBackbone=ResNet-34, Training samples=50, Pruning strategy=filter pruning2022.02 | 30.1 | 57.7 | 33.8 | |
| BP (filter)Backbone=ResNet-34, Training samples=100, Pruning strategy=filter pruning2022.02 | 27.6 | 56.7 | 33.8 | |
| BP (filter)Backbone=ResNet-34, Training samples=50, Pruning strategy=filter pruning2022.02 | 24.2 | 52.7 | 33.8 |