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Table 6 Performances of deep learning models reported in CV-based wood identification studies

From: Computer vision-based wood identification and its expansion and contribution potentials in wood science: A review

References Dataset Image type #SP/#IMG CNN model CLS %
Hafemann et al. [29] UFPR Macro 41/2942 3-ConvNeta 95.8
Micro 112/2240 3-ConvNeta 97.3
Kwon et al. [96] Softwoods Macro 5/16,865 LeNet 99.3
Kwon et al. [64] Softwoods Macro 5/33,815 Ensemble of LeNet2, LeNet3, and MiniVGG4 0.98b
Ravindran et al. [81] Meliaceae species Stereo 10/2303 VGG16 88.7c
97.5d
Tang et al. [49] FRIM collection Stereo 100/101,446 SqueezeNet 77.5
Lopes et al. [51] FWRC collection Stereo 10/1869 InceptionV4_ResNetV2 92.6
Lens et al. [72] UFPR Micro 112/2240 ResNet101 96.4
de Geus et al. [55] Brazilian species Stereo 281/– DenseNet 98.8
Ravindran [21] Melaceae species Stereo 10/–e ResNet34 81.9
96.1
Fabijanska et al. [65] European species Macro 14/312 Residual convolutional encoder network 98.7
  1. #SP: number of species; #IMG: number of images; CLS %: classification accuracy; FWRC: Forest and Wildlife Research Center at Mississippi State University; –: no number specified
  2. a3 layers deep CNN
  3. bF1 score
  4. cSpecies identification
  5. dGenus identification
  6. eAt least 5 images per specimen (total 193 wood specimens)