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Table 4 Classification networks: training time and model size

From: Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

Model Training time (s) Number of parameters Size (MB) Acc. (%)
DenseNet-121 1522.88 6955906 28.4 95.61
DenseNet-161 2157.04 26,476,418 107.1 95.12
DenseNet-169 1306.20 12,487,810 50.9 94.63
ResNet-18 546.77 11,177,536 44.8 94.63
ResNet-34 719.41 21,285,696 85.3 94.15
ResNet-50 1011.85 23,512,128 94.4 94.88
ResNet-101 1668.41 42,504,256 170.6 95.12
ResNet-152 2172.97 58,147,904 233.4 94.88
SqueezeNet-1.0 533.15 736,450 3.0 95.12
SqueezeNet-1.1 481.53 723,522 2.9 94.39
VGG-11 2382.44 128,774,530 515.1 94.88
VGG-13 2641.00 128,959,042 515.9 94.39
VGG-16 2745.00 134,268,738 537.1 95.12
VGG-19 3079.89 139,578,434 558.4 94.15
EfficientNetB0 1198.53 4,052,126 33.0 95.13
EfficientNetB1 2243.48 6,577,794 53.4 95.13
EfficientNetB2 1882.26 7,771,380 62.9 93.67
EfficientNetB3 2696.21 10,786,602 87.1 95.13
EfficientNetB4 3476.74 17,677,402 142.3 95.38
EfficientNetB5 3584.68 28,517,618 229.1 93.67
EfficientNetB6 4946.95 40,964,746 328.3 94.16
Mask R-CNN 14863.00 42,504,256 255.9 N/A
  1. The number following a network’s name denotes the number of layers in the network (as in DenseNet-121 or ResNet-101) or the version of the network (as in SqueezeNet-1.0 or EfficientNetB0). All models are trained on AWS p3.2xlarge instances. The training time it took to train each model for 200 epochs is reported in seconds (s). Model complexity is reported as the number of trainable parameters of the model, as well as the size of the model in MB. The accuracy of each model is also shown, and the best accuracy (Acc.) obtained for each type of model is highlighted in bold font