Fine-grained Image Classification on Flowers102
74.99AccuracyHydraCIL
Evaluation results
| Method | Links | |
|---|---|---|
| HydraCILk=2, Training time (min)=0.45, Energy (kWh)=0.001, Emissions (kg CO2-eq)=1.7 × 10−42026.06 | 74.99 | |
| HydraCILk=5, Training time (min)=0.46, Energy (kWh)=0.001, Emissions (kg CO2-eq)=1.7 × 10−42026.06 | 70.94 | |
| CIFNetTraining time (min)=1.41, Energy (kWh)=0.004, Emissions (kg CO2-eq)=7.7 × 10−42026.06 | 70.93 | |
| HydraCILk=20, Training time (min)=0.45, Energy (kWh)=0.001, Emissions (kg CO2-eq)=1.7 × 10−42026.06 | 67.31 | |
| RMM-FOSTERTraining time (min)=69.63, Energy (kWh)=0.337, Emissions (kg CO2-eq)=0.0682026.06 | 35.71 | |
| DERTraining time (min)=59.81, Energy (kWh)=0.321, Emissions (kg CO2-eq)=0.0652026.06 | 22.89 | |
| Fine-tuneTraining time (min)=15.91, Energy (kWh)=0.070, Emissions (kg CO2-eq)=0.0122026.06 | 20.99 |