CuriousAI / mean-teacher

A state-of-the-art semi-supervised method for image recognition
https://arxiv.org/abs/1703.01780
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applying mean teacher to my own dataset #19

Open liangzimei opened 5 years ago

liangzimei commented 5 years ago

hi, i have already achieve ~94% with 4000 labels on cifar10. But for my own three classification task, i have 160k labelled data and unlabelled data. i can not get expected results( worse than using the labelled data to train directly ). is lr strategy sensitive to it? here is my setting (finetuning on mobilenet-v1 and using 4 gpus): thanks in advance

defaults = {

    # Technical details
    'workers': 20,
    'checkpoint_epochs': 20,
    'evaluation_epochs': 5,

    # Data
    'dataset': 'my dataset',
    'train_subdir': 'train',
    'eval_subdir': 'test',

    # Data sampling
    'base_batch_size': 100,
    'base_labeled_batch_size': 50,

    # Architecture
    'arch': 'mnet1',

    # Costs
    'consistency_type': 'mse',
    'consistency_rampup': 5,
    'consistency': 20.0,
    'logit_distance_cost': .01,
    'weight_decay': 2e-4,

    # Optimization
    'lr_rampup': 0,
    'base_lr': 0.001,
    'nesterov': True,
}
YilinLiu97 commented 5 years ago

Same problem here.

fahad7033 commented 4 years ago

Did you fix the problem?

liangzimei commented 4 years ago

@fahad7033 i haven't done it in a long time, you can try to train more iters.

jetoHui520 commented 2 years ago

hi, i have already achieve ~94% with 4000 labels on cifar10. But for my own three classification task, i have 160k labelled data and unlabelled data. i can not get expected results( worse than using the labelled data to train directly ). is lr strategy sensitive to it? here is my setting (finetuning on mobilenet-v1 and using 4 gpus): thanks in advance

defaults = {

    # Technical details
    'workers': 20,
    'checkpoint_epochs': 20,
    'evaluation_epochs': 5,

    # Data
    'dataset': 'my dataset',
    'train_subdir': 'train',
    'eval_subdir': 'test',

    # Data sampling
    'base_batch_size': 100,
    'base_labeled_batch_size': 50,

    # Architecture
    'arch': 'mnet1',

    # Costs
    'consistency_type': 'mse',
    'consistency_rampup': 5,
    'consistency': 20.0,
    'logit_distance_cost': .01,
    'weight_decay': 2e-4,

    # Optimization
    'lr_rampup': 0,
    'base_lr': 0.001,
    'nesterov': True,
}

So I wonder how to create my own datasets.......

paozhuanis1 commented 3 months ago

I have the same problem about how to apply the mean teacher to my own dataset(especially for audio) and model.