Image Classification on Meta-Learning Dataset (test)
84.6AccuracyProtoNet
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| ProtoNetParameters=11.2M, Backbone=ResNet-18, Evaluation Protocol=5-way 5-shot, Hardware (Energy Measurement)=NVIDIA Jetson Nano2026.01 | 84.6 | 5.92 | 0.42 | |
| DACISParameters=2.19M, Backbone=ResNet-18, Evaluation Protocol=5-way 5-shot, Hardware (Energy Measurement)=NVIDIA Jetson Nano2026.01 | 83.2 | 0.38 | 3.24 | |
| MAMLParameters=11.2M, Backbone=ResNet-18, Evaluation Protocol=5-way 5-shot, Hardware (Energy Measurement)=NVIDIA Jetson Nano2026.01 | 82.1 | 5.92 | 0.58 | |
| Channel PruningParameters=3.36M, Backbone=ResNet-18, Evaluation Protocol=5-way 5-shot, Hardware (Energy Measurement)=NVIDIA Jetson Nano2026.01 | 77.2 | 1.45 | 1.28 | |
| Magnitude PruningParameters=3.36M, Backbone=ResNet-18, Evaluation Protocol=5-way 5-shot, Hardware (Energy Measurement)=NVIDIA Jetson Nano2026.01 | 72.3 | 1.21 | 0.98 |