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StatAssist & GradBoost: Semantic Segmentation

StatAssist & GradBoost: A Study on Optimal INT8 Quantization-aware Training from Scratch

modified version of the original code from: https://github.com/sacmehta/EdgeNets

Model

  • ESPNetV1
  • ESPNetV2
  • MobileNetV2+LRASPP
  • MobileNetV3+LRASPP

Dataset

PASCAL VOC 2012

A standard practice to train segmentation model on the PASCAL VOC is with additional images from the MS-COCO. We also follow the standard procedure.

Follow below steps to download data, including directory set-up:

  • First download the COCO and VOC data. You can do this by executing following commands (I am assuming that you are at the root directory i.e. inside EdgeNets):
cd ./data_loader/segmentation/scripts 
bash download_voc.sh 
bash download_coco.sh
  • Above commands will download the PASCAL VOC and the COCO datasets and place it in EdgeNets/vision_datasets directory.
  • Next, you need to prepare the COCO dataset in the PASCAL VOC format because you have 80 classes in COCO while PASCAL VOC has only 21 classes including background.
  • After you have successfully downloaded the COCO dataset, execute following commands to prepare COCO dataset in the PASCAL VOC format:
cd ./data_loader/segmentation
python3 coco.py
  • This processing will take few hours. Be patient.
  • That's all. You are set for training on the PASCAL VOC dataset now.

Cityscapes dataset

For your convenience, we provide bash scripts that allows you to download the dataset without using web browser. Follow below steps for downloading and setting-up the Cityscapes dataset.

  • Go to scripts directory
cd  ./data_loader/segmentation/scripts
  • Using any text editor, modify the uname and pass variables inside the download_cityscapes.sh file. These variables correspond to your user name and password for the Cityscapes dataset.
# enter user details
uname='' #
pass=''
  • After you have entered your credential, execute the download_cityscapes.sh bash script to download the data.
  • Next, you need to process Cityscapes segmentation masks for training. To do so, follow below commands:
cd ./data_loader/cityscape_scripts 
python3 process_cityscapes.py
python3 generate_mappings.py
  • Now, you are set for training on the Cityscapes dataset.

Run example

  • Train
python train.py --model mobilenetv3_RE_small--dataset city --data_path ./vision_datasets/cityscapes/ --crop_size 768 768 --batch_size 16 --lr 0.05 --scheduler poly --fp_epochs 1 --epochs 100
  • Test
python evaluate.py --model mobilenetv3_RE_small --dataset city --data_path ./vision_datasets/cityscapes/ --split val --crop_size 2048 1024 --weights_test ' '