TABLE II.
Quantitative Result of Empirical Validation
Method | Metric | Data-Set | |||
---|---|---|---|---|---|
DIC-C2DH-HeLa | Fluo-N2DH- GOWT1 |
Fluo-N2DH-SIM+ | PhC-C2DH-U373 | ||
Original(CPU) [16], [36] |
Time Cost (s/frame) | 25.58 | 115.32 | 88.74 | 39.76 |
GPU Memory (GB) | 5.92 | 5.92 | 5.92 | 5.92 | |
GPU_H [21] |
Time Cost (s/frame) | 9.83 | 37.54 | 18.60 | 10.81 |
GPU Memory (GB) | 6.16 | 6.16 | 6.16 | 6.16 | |
GPU_P [22] |
Time Cost (s/frame) | - | - | - | - |
GPU Memory (GB) | overflow | overflow | overflow | overflow | |
Ours | Time Cost (s/frame) | 2.74 | 11.24 | 11.72 | 5.64 |
GPU Memory (GB) | 8.38 | 7.24 | 6.95 | 6.82 |
5.92GB is the minimal GPU memory consumption for the deep learning based feature extraction.
Due to the GPU overflow, the time-cost is not available(−)