This folder contains reference training scripts for semantic segmentation. They serve as a log of how to train specific models and provide baseline training and evaluation scripts to quickly bootstrap research.
All models have been trained on 8x V100 GPUs.
You must modify the following flags:
--data-path=/path/to/dataset
--nproc_per_node=<number_of_gpus_available>
torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet50 --aux-loss --weights-backbone ResNet50_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet101 --aux-loss --weights-backbone ResNet101_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet50 --aux-loss --weights-backbone ResNet50_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet101 --aux-loss --weights-backbone ResNet101_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py --dataset coco -b 4 --model deeplabv3_mobilenet_v3_large --aux-loss --wd 0.000001 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py --dataset coco -b 4 --model lraspp_mobilenet_v3_large --wd 0.000001 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1