Semantic segmentation reference training scripts

This folder contains reference training scripts for semantic segmentation. They serve as a log of how to train specific models, as 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>

fcn_resnet50

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

fcn_resnet101

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

deeplabv3_resnet50

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

deeplabv3_resnet101

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

deeplabv3_mobilenet_v3_large

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

lraspp_mobilenet_v3_large

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

NetsPresso Compress Tutorial

Step 1. train model

Support models: fcn_resnet50, fcn_resnet101

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

Step 2. compress the model

Visit netspresso.ai and compress the model. You can get step by step guide from here.

Step 3. fine-tune the model

You need to set the compressed model path using --model and use --netspresso to train the compressed model.

torchrun --nproc_per_node=8 train.py --lr 0.002 --dataset coco -b 4 --model path_to_compressed_model_file --aux-loss --netspresso