README.md

Train and test on CVPPP dataset

In this example we use valid padding convolutional UNet, that means that the coloring procedure can be used in fully convolutional style.

Run training

python train_cvppp.py path_to_biological

Notes on code

Creates a batch generator

generator = train_data.create_batch_generator(30, transforms=transforms)

---

Creates a halo region function

mask_builder = dc.build_halo_mask(fixed_depth=100, margin=21, min_fragment=10)

1. fixed_depth - maximum number of object in a training batch
2. margin - size of margin (dilatation) around the object sould be odd
3. min_fragment - minimal size of an object in pixels

---

Training

model, errors = dc.train(generator=generator,
                             model=net,
                             mask_builder=mask_builder,
                             niter=10000,
                             k_neg=5.,
                             lr=1e-3,                             
                             caption=join(directory, "model"))

1. generator - batch generator
2. model - segmentation network
3. niter - number of iterations
4. k_neg - balance between positive and negative parts of loss please seen paper
5. lr - learining rate
6. caption - name of errors file and model