skirt_length.cfg

[env]
exp_dir = PATH_OF_EXPERIMENT_TO_BE_CONFIGURED
data_dir = ${exp_dir}/data
model_dir = ${exp_dir}/model
eval_dir = ${exp_dir}/eval


[data]
dataset_type = FashionaiAttributeDataset
dataset_params = {
        "train_data_folder": "PATH_OF_TRAIN_DATA_FOLDER_BE_CONFIGURED",
        "test_data_folder": "PATH_OF_TEST_DATA_TO_BE_CONFIGURED",
        "attribute": "skirt_length_labels"
    }
train_data = PATH_OF_TRAIN_CSV_TO_BE_CONFIGURED
validation_data = PATH_OF_VALIDATION_CSV_TO_BE_CONFIGURED
test_data = test_0222 PATH_OF_TEST_CSV_TO_BE_CONFIGURED
image_height = 224
image_width = 224
image_channels = 3
image_format = jpeg
batch_size = 64
shuffle_buffer_size = 10000
prefetch_batches = 10
num_data_processes = 10


[train]
net = resnet_v2
net_params = {
        "size": 50,
        "num_classes": 6
    }
loss = sparse_softmax_cross_entropy
predictions = softmax
metrics = accuracy
lr_policy = exponential_decay
lr_policy_params = {
        "base_lr": 1e-3,
        "decay_steps": 5000,
        "decay_rate": 0.75
    }
optimizer = momentum
optimizer_params = {
        "momentum": 0.9
    }
summary = normal

max_step = 50000
summary_steps = 100
model_save_steps = 1000
transfer_checkpoint = PATH_OF_PRETRAINED_MODEL_TO_BE_CONFIGURED
transfer_params = {
        "skip_variables": [
            "dense"
        ]
    }


[evaluate]
model_step = 20000
predict_saver_type = FashionaiAttributePredictSaver
predict_saver_params = {
        "attribute": "skirt_length_labels"
    }