You Only Look At CoefficienTs

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A simple, fully convolutional model for real-time instance segmentation. This is the code for our paper, and for the forseeable future is still in development.

Here's a look at our current results for our base model (33 fps on a Titan Xp and 29.8 mAP on COCO's test-dev):

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
pip install cython
pip install opencv-python pillow pycocotools matplotlib

Clone this repository and enter it:

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

If you'd like to train 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
```
If you'd like to evaluate YOLACT on test-dev, download test-dev with this script.
```
sh data/scripts/COCO_test.sh
```

Evaluation

As of April 5th, 2019 here are our latest models along with their FPS on a Titan Xp and mAP on test-dev:

Image Size	Backbone	FPS	mAP	Weights
550	Resnet50-FPN	42.5	28.2	yolact_resnet50_54_800000.pth
550	Darknet53-FPN	40.0	28.7	yolact_darknet53_54_800000.pth
550	Resnet101-FPN	33.0	29.8	yolact_base_54_800000.pth
700	Resnet101-FPN	23.6	31.2	yolact_im700_54_800000.pth

To evalute the model, put the corresponding weights file in the ./weights directory and run one of the following commands.

Quantitative Results on COCO

# Quantitatively evaluate a trained model on the entire validation set. Make sure you have COCO downloaded as above.
# This should get 29.92 validation mask mAP last time I checked.
python eval.py --trained_model=weights/yolact_base_54_800000.pth

# Output a COCOEval json to submit to the website or to use the run_coco_eval.py script.
# This command will create './results/bbox_detections.json' and './results/mask_detections.json' for detection and instance segmentation respectively.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json

# You can run COCOEval on the files created in the previous command. The performance should match my implementation in eval.py.
python run_coco_eval.py

# To output a coco json file for test-dev, make sure you have test-dev downloaded from above and go
python eval.py --trained_model=weights/yolact_base_54_800000.pth --output_coco_json --dataset=coco2017_testdev_dataset

Qualitative Results on COCO

# Display qualitative results on COCO. From here on I'll use a confidence threshold of 0.3.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.3 --top_k=100 --display

Benchmarking on COCO

# Run just the raw model on the first 1k images of the validation set
python eval.py --trained_model=weights/yolact_base_54_800000.pth --benchmark --max_images=1000

Images

# Display qualitative results on the specified image.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.3 --top_k=100 --image=my_image.png

# Process an image and save it to another file.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.3 --top_k=100 --image=input_image.png:output_image.png

# Process a whole folder of images.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.3 --top_k=100 --images=path/to/input/folder:path/to/output/folder

Video

# Display a video in real-time
# I have to work out the kinks for this one. Drawing the frame takes more time than executing the network resulting in sub-30 fps :/
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.3 --top_k=100 --video=my_video.mp4

# Process a video and save it to another file.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.3 --top_k=100 --video=input_video.mp4:output_video.mp4

As you can tell, eval.py can do a ton of stuff. Run the --help command to see everything it can do.

python eval.py --help

Training

To train, grab an imagenet-pretrained model and put it in ./weights.
- For Resnet101, download resnet101_reducedfc.pth from here.
- For Resnet50, download resnet50-19c8e357.pth from here.
- For Darknet53, download darknet53.pth 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 base config with a batch size of 8 (the default).
python train.py --config=yolact_base_config

# Trains yolact_base_config with a batch_size of 5. For the 550px models, 1 batch takes up around 1.5 gigs of VRAM, so specify accordingly.
python train.py --config=yolact_base_config --batch_size=5

# Resume training yolact_base with a specific weight file and start from the iteration specified in the weight file's name.
python train.py --config=yolact_base_config --resume=weights/yolact_base_10_32100.pth --start_iter=-1

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

Citation

If you use YOLACT or this code base in your work, please cite

@article{bolya-arxiv2019,
  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
  title     = {YOLACT: {Real-time} Instance Segmentation},
  journal   = {arXiv},
  year      = {2019},
}

Contact

For questions about our paper or code, please contact Daniel Bolya.

zllrunning/yolact