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ubuntu 18.04, tensorflow 2.0.0, opencv-python 4.2.0.32, numpy 1.18.2
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FCN(test x)
UNet (Test complete)
PSPNet(Modification Required)
PSPUNet(PSPNet + Unet, Test complete)
ICNet(Modification Required)
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Dataset - AI Hub sidewalk walking image
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train data : 38000, val data : 12800
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IMG_WIDTH = 480
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IMG_HEIGHT = 272
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n_classes = 7
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data argumentation - random_flip_horizon, random brightness
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learning rate : 1e-4 -> epoch>10 lr decay(1e-5)
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class | label |
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Background | 0 |
Bike_lane | 1 |
Caution_zone | 2 |
Crosswalk | 3 |
braille_guide_blocks | 4 |
Roadway | 5 |
Sidewalk | 6 |
I provide a pretraining model weight. (pspunet loss 0.3160 mIoU 74.5% acc 90.2%)
git clone https://github.com/JunHyeok96/Road-Segmentation.git
cd road_segmentation
python3 demo.py #before run command, you have to set your test video path in demo.py
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TEST GPU - RTX2060 SUPER
model | accuracy | loss | mIoU | FPS | Size |
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PSPUnet | 90.2% | 0.3160 | 74.5% | 24.8 | 39.6MB |
UNet | 89.1% | 0.3570 | 70.9% | 22.7 | 131MB |