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Sketch Classification

A PyTorch Implementation for Sketch Classification Networks.

Model Configuration

  • Optimizer
    • Adam

DataSet

TU-Berlin sketch dataset

Model input_size
(raw size)* 1111 * 1111
AlexNet 224 * 224
SketchANet 225 * 225
ResNet18 224 * 224
ResNet34 224 * 224
ResNet50 224 * 224
DenseNet121 224 * 224
Inception_v3 299 * 299

Model Parameters

Model lr clip_grad_norm(max_norm) learning rate decay weight_decay
AlexNet(pretrained) 2e-4 -- 20 0.0005
AlexNet(scratch) 2e-5 0.5 - 100.0 30 0.0005
SketchANet(DogsCats)* 2e-5 0.5 - 1.0 30 0.0005
SketchANet(scratch) 2e-5 0.5 - 100.0 800 0.0001 - 0.0003
ResNet18(pretrained) 2e-4 -- 20 0.0005
ResNet34(pretrained) 2e-4 -- 20 0.0001
ResNet50(pretrained) 2e-4 -- 20 0.0005
DenseNet121(pretrained) 2e-4 -- 20 0.0005
Inception_v3(pretrained) 2e-4 -- 30 0.0005
  • *This is for test Model.

Model Result

Train Set

Model Prec@1 Prec@5
AlexNet(pretrained) 93.4455 99.787
AlexNet(scratch) 99.3024 99.988
SketchANet(scratch) 86.3166 98.667
ResNet18(pretrained) 96.9899 99.954
ResNet34(pretrained) 97.1048 99.954
ResNet50(pretrained) 98.3049 99.988
DenseNet121(pretrained) 91.4301 99.596
Inception_v3(pretrained) 91.8802 99.706

Test Set

Model Prec@1 Prec@5
Human 73.1 --
AlexNeti 68.6 --
AlexNetii 77.29 --
GoogLeNetii 80.85 --
AlexNet(pretrained) 70.850 90.050
AlexNet(scratch) 53.850 78.000
SketchANet(scratch) 68.700 88.900
ResNet18(pretrained) 77.800 94.650
ResNet34(pretrained) 79.100 95.050
ResNet50(pretrained) 78.300 95.300
DenseNet121(pretrained) 77.550 93.500
Inception_v3(pretrained) 76.550 93.750
    1. Sketch-a-Net that Beats Humans
    2. The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies

DPN, ShuffleNetG2, SENet18

Tools

  • GetImageMean_Std

Get image dataset mean and standard deviation.

  • SplitDataset

Split image dataset according to the train and val record txt file.

  • ListAllImageName

Get all image name in dataset.