Dev lgbm (#147) (#152) * initial restructure * thresholds on unet output * added gmean tta, experimented with thresholding (#125) * feature exractor and lightgbm * pipeline is running ok * tmp commit * lgbm ready for tests * tmp * faster nms and feature extraction * small fix * cleaning * Dev repo cleanup (#138) * initial restructure * clean structure (#126) * clean structure * correct readme * further cleaning * Dev apply transformer (#131) * clean structure * correct readme * further cleaning * resizer docstring * couple docstrings * make apply transformer, memory cache * fixes * postprocessing docstrings * fixes in PR * Dev repo cleanup (#132) * cleanup * remove src. * Dev clean tta (#134) * added resize padding, refactored inference pipelines * refactored piepliens * added color shift augmentation * reduced caching to just mask_resize * updated config * Dev-repo_cleanup models and losses docstrings (#135) * models and losses docstrings * small fixes in docstrings * resolve conflicts in with TTA PR (#137) * refactor in stream mode (#139) * hot fix of mask_postprocessing in tta with new make transformer * finishing merge * finishing merge v2 * finishing merge v3 * finishing merge v4 * tmp commit * lgbm train and evaluate pipelines run correctly * something is not yes * fix * working lgbm training with ugly train_mode=True * back to pipelines.py * small fix * preparing PR * preparing PR v2 * preparing PR v2 * fix * fix_2 * fix_3 * fix_4

Dockerfile dropped->waiting for deployment specs 

Dev (#121) * Dev fix generate_metadata (#106) * fix generate_metadata * temp fix in callbacks * Dev loader refactor (#111) * refactored loaders, fixed weighed loss calculation * updated config * reverted resnet 152 to 32 filters with no dropout * removed print * back to 1000 valid size * back to old batch size * back other params * config * fix bug in loaders (#112) * fix validation bug (#115) * Dev tta (#116) * scheleton added * transformation and inverse done * tta working * Dev validation using mAP precision (#117) * mege TTA with mAP validation * mAP validation * fix mAP valid in eval (#120)

Dev (#105) * fix steps issues #48 and #49 * prepare sum of distances, not distances to 2 closest objects * back to const erosion * fixes * fix * [:-4] -> os.path.splitext() * loss weighted by size of the object * prepare masks, distances and sizes * cleaning * adapt models and loaders to handle size matrix and calculate size weights * adapt models and loaders to handle size matrix and calculate size weights v2 * fix pipelines.py * fix some issues with calculating size-weighted loss * cleaning * update mean and std * fixes * clean * fix recall in evaluation * fix bug in erosion (#91) * Dev mosaic padding inference (#81) * added mosaic seq, unet_mosaic pipe, mosaic loader * added unet_weighted * dropped input resize at inference * dropped rescaling in loader, fixed postpro cropping * local dev * updated dilation/erosion, joined pipelines * dropped unet mask saving * added replication padding * renamed mosaic->padded, moved params to configs * padding->inference_padding * config updates * refactored padded unet * refactored unet_padding * Dev dice loss (#89) * fix size weights * add mixed dice + weighted ce loss * fixes * parametrize loss weights * remove get_datagen function overriding * dice loss per channel, some fixes * fixes and smooth added to Dice loss instead of eps * fixes and smooth added to Dice loss and eps, and parametrized * sigmoid -> softmax in dice loss * softmax2d * move softmax to models.py * parametrize softmax and sigmoid in dice loss * Dev mask prep speed up (#94) * distributed mask/distance/size generation added * dropped deprecated * dropped mask param * Dev random crop (#97) * local * added random cropping, refactored augmentations * Dev borders and dilation in preprocessing (#96) * merge multithread * preparing borders * fix PR #96 and add update metadata generation * Dev deeper archs (#102) * dropped mask param * added deeper resnets and spatial2d dropout * updated config * fixed casting * updated index * fix evaluate, add score builder in stream mode (#104) * added initial version * added simple evaluate on checkpoint script * updated config * added neptune file definition * fixed conflicts

Merge pull request #51 from minerva-ml/dev Evaluation in chunks added, erosion pre - dilation post approach added, multiclass problem definition enabled

Update README.md Typo in readme