AlvaradoLabSegmentation

Dataset Tools:

Use Python script to separate cropped segmentation parts and use it for semantic segmentation labels python -m ecology_semantic_segmentation.dataset.utils

After finding too many such cases (~150 manually fixed examples), I have FINALLY decided to write a program to solve the problem! python -m ecology_semantic_segmentation.dataset.bbox_masks_problem

U-Net based Semantic Segmentation

Using U-Net models from segmentation_models_pytorch pip install segmentation_models_pytorch

Datasets:

ML Training Set - AlvaradoLab annotated data - composite segmentation Cichlid Collection - AlvaradoLab annotated data - composite segmentation SUIM - semantic segmentation with >1 fish per image Deep Fish - fish_tray_images - Accurately labeled sharp masks - Semantic segmentation with >1 fish per image

Available Pre-trained Backbones:

Resnet34 Resnet50 DeepLabv3Plus

Link to trained models: https://drive.google.com/drive/folders/1jrUJtpxR8WvUf3td9nWXMIEdPmsgK_gp?usp=share_link

Training script:

Use SAMPLE=1, SAMPLE=0, IMG_SIZE=256, ORGANS="whole_body" flags to control training and debugging training code Using batch sizes as multiples of 9 gives most efficient use of GPU space

ORGANS=whole_body,ventral_side,dorsal_side python -m ecology_semantic_segmentation.train_multiclass --batch_size 54 python -m ecology_semantic_segmentation.train --batch_size 54

Learning Rate Scheduling for Adam: 0.0003 to start, re-define for every checkpoint resume manually using script parameter

Testing Script:

Uses Dice score for accuracy python -m ecology_semantic_segmentation.test --models <MODELDIR>

4-Connected lines from Photoshop annotation + 8-Connected lines from cv2 Polygon annotation = Anti-Aliased edges in CNN model result

Reasons:

1. high-resolution
2. Supersampling
3. Intensity decisions based on object overlap
4. Line intensity assignment differences between straight and diagonal lines

https://www.geeksforgeeks.org/antialiasing/

#TODO: DEEPSUPERVISION CODE!