# Summary of commands - uncomment one and run this script #### Manual Download: It is expected that the dataset is manually downloaded and kept in the folder specified agaianst the --data_path option. ## ===================================================================================== ## Training ## ===================================================================================== #### Cityscapes Semantic Segmentation - Training with MobileNetV2+DeeplabV3Lite #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth #### Cityscapes Semantic Segmentation - Training with MobileNetV2+FPN #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth #### Cityscapes Semantic Segmentation - MobileNetV2+FPN - no aspp model, stride 64 model - Low Complexity Model #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_mobilenetv2_tv_fd --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth #### Cityscapes Semantic Segmentation - Training with MobileNetV2+DeeplabV3Lite, Higher Resolution #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth #### Cityscapes Semantic Segmentation - original fpn - no aspp model, stride 64 model, Higher Resolution - Low Complexity Model #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_mobilenetv2_tv_fd --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth #### Cityscapes Semantic Segmentation - Training with ResNet50+DeeplabV3Lite #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_resnet50 --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth #### Cityscapes Semantic Segmentation - Training with ResNet50+FPN #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_resnet50 --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth #### Cityscapes Semantic Segmentation - Training with FD-ResNet50+FPN - High Resolution #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_resnet50 --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth #### Cityscapes Semantic Segmentation - Training with ResNet50_p5+DeeplabV3Lite (ResNet50 encoder with half the channels): deeplabv3lite_resnet50_p5 & deeplabv3lite_resnet50_p5_fd #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name deeplabv3lite_resnet50_p5 --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained "./data/modelzoo/pretrained/pytorch/imagenet_classification/resnet50-0.5_b256_lr0.1_step30_inception-aug(0.08-1.0)_epoch(92of100)_1gmac_(72.05%)/model_best.pth.tar" #### Cityscapes Semantic Segmentation - Training with FD-ResNet50+FPN - High Resolution - Low Complexity Model #python ./scripts/train_segmentation_main.py --dataset_name cityscapes_segmentation --model_name fpn_pixel2pixel_aspp_resnet50_fd --data_path ./data/datasets/cityscapes/data --img_resize 768 1536 --rand_crop 512 1024 --output_size 1024 2048 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/resnet50-19c8e357.pth #### VOC Segmentation - Training with MobileNetV2+DeeplabV3Lite #python ./scripts/train_segmentation_main.py --dataset_name voc_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/voc --img_resize 512 512 --output_size 512 512 --gpus 0 1 \ #--pretrained https://download.pytorch.org/models/mobilenet_v2-b0353104.pth ## ===================================================================================== ## Validation ## ===================================================================================== #### Validation - Cityscapes Semantic Segmentation - Validation with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ?? #python ./scripts/train_segmentation_main.py --evaluate True --dataset_name cityscapes_segmentation --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained ?? #### Inference - Cityscapes Semantic Segmentation - Inference with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ?? #python ./scripts/infer_segmentation_main.py --dataset_name cityscapes_segmentation_measure --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/cityscapes/data --img_resize 384 768 --output_size 1024 2048 --gpus 0 1 \ #--pretrained ??? #### Validation - VOC Segmentation - Validation with MobileNetV2+DeeplabV3Lite - populate the pretrained filename in ?? #python ./scripts/train_segmentation_main.py --evaluate True --dataset_name voc_segmentation_measure --model_name deeplabv3lite_mobilenetv2_tv --data_path ./data/datasets/voc --img_resize 512 512 --output_size 512 512 --gpus 0 1 #--evaluate True --pretrained ???