Fast-SCNN

A PyTorch implementation of Fast-SCNN based on BMVC 2019 paper Fast-SCNN: Fast Semantic Segmentation Network.

Requirements

• Anaconda
• PyTorch

conda install pytorch torchvision cudatoolkit=10.0 -c pytorch

• thop

pip install thop

• opencv

pip install opencv-python

• cityscapesScripts

pip install git+https://github.com/mcordts/cityscapesScripts.git

Expected dataset structure for Cityscapes:

cityscapes/
  gtFine/
    train/
      aachen/
        color.png, instanceIds.png, labelIds.png, polygons.json,
        labelTrainIds.png
      ...
    val/
  leftImg8bit/
    train/
    val/

Set environment variable CITYSCAPES_DATASET firstly, for example:

export CITYSCAPES_DATASET=/home/data/cityscapes

and then run createTrainIdLabelImgs.py to creat labelTrainIds.png.

Usage

Train model

python train.py --crop_h 512 --crop_w 1024
optional arguments:
--data_path                   Data path for cityscapes dataset [default value is '/home/data/cityscapes']
--crop_h                      Crop height for training images [default value is 1024]
--crop_w                      Crop width for training images [default value is 2048]
--batch_size                  Number of data for each batch to train [default value is 12]
--save_step                   Number of steps to save predicted results [default value is 5]
--epochs                      Number of sweeps over the dataset to train [default value is 100]

Set environment variable CITYSCAPES_DATASET and CITYSCAPES_RESULTS firstly, for example:

export CITYSCAPES_DATASET=/home/data/cityscapes
export CITYSCAPES_RESULTS=/home/code/Fast-SCNN/results

and then run evalPixelLevelSemanticLabeling.py to eval the predicted segmentation.

Eval model

python viewer.py --model_weight 512_1024_model.pth
optional arguments:
--data_path                   Data path for cityscapes dataset [default value is '/home/data/cityscapes']
--model_weight                Pretrained model weight [default value is '1024_2048_model.pth']
--input_pic                   Path to the input picture [default value is 'test/berlin/berlin_000000_000019_leftImg8bit.png']

Results

The experiment is conducted on one NVIDIA Tesla V100 (32G) GPU, and there are some difference between this implementation and official implementation:

1. The scales of Multi-Scale Training are (0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0);
2. No color channels noise and brightness used;
3. No auxiliary losses at the end of learning to downsample and the global feature extraction modules used;
4. The training epochs is 100;
5. Adam optimizer with learning rate 1e-3 is used to train this model;
6. No Polynomial Learning Scheduler used.

Params (M)	FLOPs (G)	FPS	Pixel Accuracy	Class mIOU	Category mIOU	Download
1.14	6.92	197	81.8	58.0	81.7	model | eg6a

The left is input image, the middle is ground truth segmentation, and the right is model's predicted segmentation.