UNet3plus

UNet3+

Reference

[2021] PVT V2: Improved Baselines with Pyramid Vision Transformer

[2020] ConvNext: A ConvNet for the 2020s

[2020] UNET 3+: A Full-Scale Connected UNet for Medical Image Segmentation (ICASSP 2020)

Dependencies

• Python == 3.9
• PyTorch >= 1.10.2
• Torchvision >= 0.11.3
• CUDA >= cu113
• numpy >= 1.20.3
• Pillow >= 8.3.1
• tensorboard >= 2.9.1
• tqdm == 4.62.3
• scikit-learn == 1.2.2
• timm == 0.6.13
• opencv-python == 4.7.0.72
• albumentations == 1.3.0
• segmentation-models-pytorch ==0.3.2
• pandas == 2.0.1

augmentations

augmentations are tuned for the histopathological images - Equalize, HueSaturationValue, ColorJitter, Blur, RandomBrightnessContrast, ChannelShuffle - Transpose, RandomRotate90, Flip

Training methodlogy

- Mixed Precision 
- Gradient Accumalation
- Label Smoothing
- Gradient Clipping

Output

• checkpoint.pth.tar
• bestmodel.pth.tar if best model is found
• commandlines.txt
• log.txt
• tensorboard file to track the training curves
• train_dice_score .mat and val_dice_score.mat files which consists of path to each image and image mean dice

Usage

from utils.models import Unet3plus, Unet3plusGlcm, Unet3plus_deepsupervision

model = Unet3plus(num_classes = num_classes, encoder = config.encoder)

Run locally

Note : Use Python 3

Training

python train.py 


Train the UNet on images and target masks
Update the config.py file

optional arguments:
	"Segmentation Training"
	num_workers = 8
	epochs = 50
	train_batch = 8
	lr = 0.0005
	weight_decay = 0.0005
	checkpoint = 'exp1'
	resume = '' #path to checkpoint
	gpu = 0
	seed = 42
	clip = None # else 0.99999
	size = 512
	ignore_label = 5 # should keep same as num_classes
	accum_iter=1 #Gradient Accumalation is True if accum_iter>1
	label_sm = 0.08
	freeze_backbone = False
	encoder = 'convnext_tiny'
	num_classes  = 5
	train_image_path = '../dataset/train/images/'
	train_mask_path = '../dataset/train/masks/'
	validation_image_path = '../dataset/val/images/'
	validation_mask_path = '../dataset/val/masks/'              

The input images and target masks should have the same name. The ignore label in mask should have label value = 255