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define_config.py
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import yaml
with open("car/data.yaml", 'r') as stream:
num_classes = str(yaml.safe_load(stream)['nc'])
##write custom model .yaml
# you can configure this based on other YOLOv5 models in the models directory
with open('models/custom_car_yolov5s.yaml', 'w') as f:
# parameters
f.write('nc: ' + num_classes + '\n')
# f.write('nc: ' + str(len(class_labels)) + '\n')
f.write('depth_multiple: 0.33' + '\n') # model depth multiple
f.write('width_multiple: 0.50' + '\n') # layer channel multiple
f.write('\n')
f.write('anchors:' + '\n')
f.write(' - [10,13, 16,30, 33,23] ' + '\n')
f.write(' - [30,61, 62,45, 59,119]' + '\n')
f.write(' - [116,90, 156,198, 373,326] ' + '\n')
f.write('\n')
f.write('backbone:' + '\n')
f.write(' [[-1, 1, Focus, [64, 3]],' + '\n')
f.write(' [-1, 1, Conv, [128, 3, 2]],' + '\n')
f.write(' [-1, 3, Bottleneck, [128]],' + '\n')
f.write(' [-1, 1, Conv, [256, 3, 2]],' + '\n')
f.write(' [-1, 9, BottleneckCSP, [256]],' + '\n')
f.write(' [-1, 1, Conv, [512, 3, 2]], ' + '\n')
f.write(' [-1, 9, BottleneckCSP, [512]],' + '\n')
f.write(' [-1, 1, Conv, [1024, 3, 2]],' + '\n')
f.write(' [-1, 1, SPP, [1024, [5, 9, 13]]],' + '\n')
f.write(' [-1, 6, BottleneckCSP, [1024]],' + '\n')
f.write(' ]' + '\n')
f.write('\n')
f.write('head:' + '\n')
f.write(' [[-1, 3, BottleneckCSP, [1024, False]],' + '\n')
f.write(' [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]],' + '\n')
f.write(' [-2, 1, nn.Upsample, [None, 2, "nearest"]],' + '\n')
f.write(' [[-1, 6], 1, Concat, [1]],' + '\n')
f.write(' [-1, 1, Conv, [512, 1, 1]],' + '\n')
f.write(' [-1, 3, BottleneckCSP, [512, False]],' + '\n')
f.write(' [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]],' + '\n')
f.write(' [-2, 1, nn.Upsample, [None, 2, "nearest"]],' + '\n')
f.write(' [[-1, 4], 1, Concat, [1]],' + '\n')
f.write(' [-1, 1, Conv, [256, 1, 1]],' + '\n')
f.write(' [-1, 3, BottleneckCSP, [256, False]],' + '\n')
f.write(' [-1, 1, nn.Conv2d, [na * (nc + 5), 1, 1, 0]],' + '\n')
f.write('\n')
f.write(' [[], 1, Detect, [nc, anchors]],' + '\n')
f.write(' ]' + '\n')
print('custom model config written!')