Open amcinto opened 6 years ago
As I remember, on MNIST it will take few hours, but much more on Imagenet. (few days?)
Training should be run on GPU if you want it to run fast.
That's just an argument, but you're suggestion makes sense. I will change that.
I run the command python train.py --dataset=mnist224 --model-conf=lcnnfast
and the learning starts but there is a display of everything within the conf/alexnet.yaml file. I'm not sure why its doing that because unless I comment parts out the epochs still display top 1% and top 5% accuracy. Which it shouldn't in my opinion. Why is that?
Hello, I have been working with the train.py and trying to reproduce some of the results that you have had. I had some questions though.
I was also wondering if in the data_feeder.py was the "lcnntest" supposed to be "lcnnfast"?
if __name__ == '__main__': parser = argparse.ArgumentParser(description='Tensorflow Training using LCNN.') parser.add_argument('--conf', default='./confs/alexnet.yaml', help='configuration file path') parser.add_argument('--model-conf', default='lcnntest', help='lcnnbest, lcnn0.9, normal') parser.add_argument('--dataset', default='mnist224', help='mnist, mnist224, ilsvrc2012') parser.add_argument('--conv', default='lcnn', help='lcnn, conv') parser.add_argument('--path-ilsvrc2012', default='/data/public/ro/dataset/images/imagenet/ILSVRC/2012/object_localization/ILSVRC/') parser.add_argument('--logpath', default=LOG_DIR) parser.add_argument('--restore', type=str, default='')
Sorry for the many questions but I was having trouble and would appreciate any help!