# 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
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet50 --aux-loss
```

## fcn_resnet101
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet101 --aux-loss
```

## deeplabv3_resnet50
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet50 --aux-loss
```

## deeplabv3_resnet101
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet101 --aux-loss
```

## deeplabv3_mobilenet_v3_large
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --dataset coco -b 4 --model deeplabv3_mobilenet_v3_large --aux-loss --wd 0.000001
```

## lraspp_mobilenet_v3_large
```
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py --dataset coco -b 4 --model lraspp_mobilenet_v3_large --wd 0.000001
```