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NetFrame is a flexible and scalable deep-learning framework to build segmentation model on large scale pathological images.

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NetFrame is a flexible and scalable deep-learning framework to build segmentation model on large scale pathological images.


Environment

python2.7

pip install -r requirements.txt

Configuration

NetFame uses a configuration file of yaml to set up all the parameters.

Argument Comment Example
random_mirror Randomly mirrors the image True
color_jitter Randomly distorts color True
random_blur Randomly blurs the image True
random_scale Randomly scales the images between 0.75 to 1.25 times of the original size. True
num_classes Number of classes 2
learning_rate Initial learning rate 0.003
lr_decay_step The number of steps in a learning rate decay cycle 15000
lr_decay_ratio Learning rate decay ratio 0.5
batch_size Batch size 32
resnet_layer Number of layers of the ResNet backbone 50
input_size Input size 320
patch_size Patch size 320
l2_loss_lambda Factor for weight decay loss !!float 1e-5
restore_iters Restored model corresponding to the iterations 0
log_label Logging the metrics of the label 1
save_step The number of steps model is saved in 2000
max_epoch The maxmum training epoch 10
gpu Specified the list of gpus to use [0,1]

Build a New Model

Training

  1. Create a configuration file setting all the parameters used for training;

  2. Implemente the data and model module under the project directory by inheriting the default parent class;

  3. Run the training program, eg:

    python main.py \
        --config_file ./config/stomach/config_stomach_v0.yaml \
        --version v0_0 \
        --mode train \
    

Inference

  1. Change the value of argument mode from train to test;

  2. Specify the number of iterations of the model to be tested;

  3. Run the test program, eg:

    python main.py \
        --config_file ./config/stomach/config_stomach_v0.yaml \
        --version v0_0 \
        --mode test \
        --restore_iters 50000
    

About

NetFrame is a flexible and scalable deep-learning framework to build segmentation model on large scale pathological images.

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