giorking / yolact

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A model for realtime instance segmentation. Yolact is still in development, so all of this is still subject to change. Here's a look at our current results (31.5 fps on a Titan Xp):

Because the acronym YOLACT doesn't fit our method anymore, the current name for our model is "Single Shot Segmenter". This, however, is also subject to change.

Installation

• Set up a Python3 environment.
• Install Pytorch 1.0.1 (or higher) and TorchVision.
• Install some other packages:

  # Cython needs to be installed before pycocotools
  pip3 install cython
  pip3 install opencv-python pillow pycocotools matplotlib 

• Clone this repository and enter it:

  git clone https://github.com/dbolya/yolact.git
  cd yolact

• Download the COCO dataset and the 2014/2017 annotations. Note that this script will take a while and dump 21gb of files into ./data/coco.

  sh data/scripts/COCO.sh

Training

• To train, grab a pretrained VGG16 or Resnet101 model and put it in ./weights.
  • For VGG16, download vgg16_reducedfc.pth from here.
  • For Resnet101, download resnet101_reducedfc.pth from here.
  • For Resnet101 with Group Norm, download R-101-GN.pkl from here.
• Run one of the training commands below.
  • Note that you can press ctrl+c while training and it will save an *_interrupt.pth file at the current iteration.
  • All weights are saved in the ./weights directory by default with the file name <config>_<epoch>_<iter>.pth.

# Trains using the default config (whatever it is at the moment) and with batch size 8 (the default).
python3 train.py

# Trains yrm10_config with a batch_size of 5 (suprise). For the 550px models, 1 batch takes up around 1.8 gigs of VRAM, so specify accordingly.
python3 train.py --config=yrm10_config --batch_size=5

# See data/config.py for a list of configs (they end with _config)
# Note that the --config paramater is eval'd so you can do clever stuff like
python3 train.py --config="yolact_resnet101_config.copy({'max_size': 600})" --batch_size=5

# Resume training config yrm10 with a specific weight file and start from the iteration specified in the weight file's name.
python3 train.py --config=yrm10_config --resume=weights/yrm10_10_32100.pth --start_iter=-1

# Use the help option to see a description of all available command line arguments
python3 train.py --help

Evaluation

# Quantitatively evaluate a trained model on the entire validation set
# Note: the config will be parsed from the model name. If you don't want that, specify a config with --config
python3 eval.py --trained_model=weights/yrm10_10_32100.pth

# Restrict the number of images evaluated to 100 (since the full validation set will take a while)
python3 eval.py --trained_model=weights/yrm10_10_32100.pth --max_images=100

# Output a COCOEval json to submit to the website or to use the run_coco_eval.py script
python3 eval.py --trained_model=weights/yrm10_10_32100.pth --output_coco_json

# Display qualitative results
python3 eval.py --trained_model=weights/yrm10_10_32100.pth --display

# See the help option for more details (there are a lot of extra command line arguments)
python3 eval.py --help