Table 3.
The training time and counting time of deep learning methods
| Related work | Model | GPU | Training time | Counting time | Accuracy |
|---|---|---|---|---|---|
| Blackburn et al. (1998) | ANN | – | – | 100 images per hour | 95% |
| Shabtai et al. (1996) | ANN | PC-Vision Plus | – | – | – |
| Embleton et al. (2003) | ANN | Pentium processor | – | 75 images per minutes | >90% |
| Hongwei (2012) | BPNN | – | – | < 1 hour | 95% |
| Shenglang et al. (2008) | BPNN | SDK-2000 | – | 1 image in 3 seconds | Student’s t test p>0.05 |
| Rong et al. (2006) | BPNN | – | – | – | – |
| Ferrari et al. (2015) | CNN | Nvidia Titan Black | 50000 iterations in 3 hours | – | 92.8% |
| Ferrari et al. (2017) | CNN | Nvidia Titan X | 50000 iterations in 1 hours | – | 92.1% |
| Tamiev et al. (2020) | cCNN | NVIDIA Quadro K620 | – | 3.8 times faster | 86% |
| Albaradei et al. (2020) | SRNet | – | – | – | RMSE = 22.38 |