Hardware environment:
- GPU: V100 * 8
- CPU: Intel Xeon
Software environment:
- Ptyhon 3.7
- PyTorch 1.3
- CUDA 10.0
| Method | Model Name | Accuracy | Paras(M) |
|---|
| EfficientNet | B0 | 76.83 | |
| B4 | 82.8 | |
| B8 672 | 85.7 | 88 |
| B8 832 | 85.8 | 88 |
| DARTS | - | 73.30 | - |
| AmeobaNet-A | - | 83.90 | - |
| ProxylessNAS | - | 75.10 | - |
| StacNAS | - | 76.78 | - |
| Method | Model Name | #Paras(M) | Accuracy |
|---|
| CARS | CARS-A | 1.402 | 95.92 |
| CARS-B | 1.697 | 96.58 |
| CARS-C | 1.913 | 96.74 |
| CARS-D | 2.225 | 97.05 |
| CARS-E | 2.408 | 97.25 |
| CARS-F | 3.767 | 97.30 |
| CARS-G | 4.377 | 97.38 |
| CARS-H | 4.506 | 97.43 |
| DARTS | - | 3.30 | 97.24 |
| NSGANet | - | 3.30 | 97.25 |
| SNAS | Aggressive | 2.30 | 96.90 |
| Mild | 2.90 | 97.02 |
| AmeobaNet-A | - | 3.10 | 96.88 |
| ProxylessNAS | - | 5.70 | 97.92 |
| StacNAS | - | 3.90 | 97.98 |
| Method | Model Name | FLOPs(G) | Params | F1 Score |
|---|
| AutoLane | CULane-S | 2.09 | 4.57 | 71.5 |
| CULane-M | 8.54 | 6.6 | 74.6 |
| CULane-L | 2.08 | 7.32 | 75.2 |
| SCNN | - | 328.4 | - | 71.6 |
| SAD | - | 162.2 | - | 71.8 |
| PointLane | - | 25.1 | - | 70.2 |
| Method | Model Name | Model Size/M | Flops/G | PSNR | SSIM |
|---|
| ESR-EA | ESRN-V-1 | 1.32 | 40.616 | 37.79 | 0.9566 |
| ESRN-V-2 | 1.31 | 40.21 | 37.84 | 0.9569 |
| ESRN-V-3 | 1.31 | 41.676 | 37.79 | 0.9570 |
| ESRN-V-4 | 1.35 | 40.17 | 37.83 | 0.9567 |
| SR_EA | M2Mx2-A | 3.20 | 196.27 | 38.06 | 0.9588 |
| M2Mx2-B | 0.61 | 35.03 | 37.73 | 0.9562 |
| M2Mx2-C | 0.24 | 13.49 | 37.56 | 0.9556 |
| SRCNN | - | - | 52.7 | 36.66 | 0.9524 |
| CARN-M | - | - | 91.2 | 37.53 | 0.9583 |
| FALSR-B | - | 0.32 | 74.70 | 37.61 | 0.9585 |
| Method | Model Name | Model Size/M | Flops/G | PSNR | SSIM |
|---|
| ESR-EA | ESRN-V-1 | 1.32 | 40.616 | 33.37 | 0.8887 |
| ESRN-V-2 | 1.31 | 40.21 | 33.37 | 0.8911 |
| ESRN-V-3 | 1.31 | 41.676 | 33.35 | 0.8878 |
| ESRN-V-4 | 1.35 | 40.17 | 33.35 | 0.8902 |
| SR_EA | M2Mx2-A | 3.20 | 196.27 | 33.65 | 0.8943 |
| M2Mx2-B | 0.61 | 35.03 | 33.32 | 0.8870 |
| M2Mx2-C | 0.24 | 13.49 | 33.13 | 0.8829 |
| SRCNN | - | - | 52.7 | 32.42 | 0.9063 |
| CARN-M | - | - | 91.2 | 33.26 | 0.9141 |
| FALSR-B | - | 0.32 | 74.70 | 33.29 | 0.9143 |
| Method | Model Name | Model Size/M | Flops/G | PSNR | SSIM |
|---|
| ESR-EA | ESRN-V-1 | 1.32 | 40.616 | 32.09 | 0.8802 |
| ESRN-V-2 | 1.31 | 40.21 | 32.08 | 0.8810 |
| ESRN-V-3 | 1.31 | 41.676 | 32.05 | 0.8789 |
| ESRN-V-4 | 1.35 | 40.17 | 32.06 | 0.8810 |
| SR_EA | M2Mx2-A | 3.20 | 196.27 | 32.20 | 0.8842 |
| M2Mx2-B | 0.61 | 35.03 | 32.00 | 0.8989 |
| M2Mx2-C | 0.24 | 13.49 | 31.89 | 0.8783 |
| SRCNN | - | - | 52.7 | 31.26 | 0.8879 |
| CARN-M | - | - | 91.2 | 31.92 | 0.8960 |
| FALSR-B | - | 0.32 | 74.70 | 31.97 | 0.8967 |
| Method | Model Name | Model Size/M | Flops/G | PSNR | SSIM |
|---|
| ESR-EA | ESRN-V-1 | 1.32 | 40.616 | 31.65 | 0.8814 |
| ESRN-V-2 | 1.31 | 40.21 | 31.69 | 0.8829 |
| ESRN-V-3 | 1.31 | 41.676 | 31.47 | 0.8803 |
| ESRN-V-4 | 1.35 | 40.17 | 31.58 | 0.8814 |
| SR_EA | M2Mx2-A | 3.20 | 196.27 | 32.20 | 0.8948 |
| M2Mx2-B | 0.61 | 35.03 | 31.37 | 0.8796 |
| M2Mx2-C | 0.24 | 13.49 | 30.92 | 0.8717 |
| SRCNN | - | - | 52.7 | 29.50 | 0.8946 |
| CARN-M | - | - | 91.2 | 31.23 | 0.9144 |
| FALSR-B | - | 0.32 | 74.70 | 31.28 | 0.9191 |
| Method | Model Name | Model Size/M | Flops/G | Params/K | mIOU |
|---|
| Adelaide_EA | - | 10.6 | 0.5784 | 3822.14 | 0.7602 |
| MV2 + LW RefineNet | - | - | 0.92 | 4163 | 0.7313 |
| Method | Model Name | Model Size/M | Accuracy |
|---|
| auto_group | - | 111 | 0.790 |
| auto_fis | - | 500 | 0.788 |
| FM | - | 111 | 0.7793 |
| DeepFM | - | 111 | 0.7836 |