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car-detection-bayes
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updates

This commit is contained in:
glenn-jocher 2019-07-01 17:17:29 +02:00
parent cf51cf9c99
commit f43ee6ef94
1 changed files with 5 additions and 5 deletions
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train.py
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@ -307,7 +307,7 @@ def print_mutation(hyp, results):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=1, help='number of epochs')
parser.add_argument('--epochs', type=int, default=100, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=8, help='batch size')
parser.add_argument('--accumulate', type=int, default=8, help='number of batches to accumulate before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
@ -320,8 +320,8 @@ if __name__ == '__main__':
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--giou', action='store_true', help='use GIoU loss instead of xy, wh loss')
parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution')
parser.add_argument('--cloud_evolve', action='store_true', help='--evolve from a central source')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--cloud_evolve', action='store_true', help='evolve hyperparameters from a cloud source')
parser.add_argument('--var', default=0, type=int, help='debug variable')
opt = parser.parse_args()
print(opt)
@ -347,7 +347,7 @@ if __name__ == '__main__':
for _ in range(gen):
# Get best hyperparamters
x = np.loadtxt('evolve.txt', ndmin=2)
x = x[x[:, 2].argmax()] # select best mAP for fitness (col 2)
x = x[x[:, 2].argmax()] # select best mAP as genetic fitness (col 2)
for i, k in enumerate(hyp.keys()):
hyp[k] = x[i + 5]
@ -356,7 +356,7 @@ if __name__ == '__main__':
s = [.2, .2, .2, .2, .2, .2, .2, .2, .2 * 0, .2 * 0, .05 * 0, .2 * 0] # fractional sigmas
for i, k in enumerate(hyp.keys()):
x = (np.random.randn(1) * s[i] + 1) ** 2.0 # plt.hist(x.ravel(), 300)
hyp[k] *= float(x) # vary by about 30% 1sigma
hyp[k] *= float(x) # vary by 20% 1sigma
# Clip to limits
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']