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 papers:

• YOLACT: Real-time Instance Segmentation
• YOLACT++: Better Real-time Instance Segmentation

YOLACT++ (v1.2) released! (Changelog)

YOLACT++'s resnet50 model runs at 33.5 fps on a Titan Xp and achieves 34.1 mAP on COCO's test-dev (check out our journal paper here).

In order to use YOLACT++, make sure you compile the DCNv2 code. (See Installation)

For a real-time demo, check out our ICCV video:

Some examples from our YOLACT base model (33.5 fps on a Titan Xp and 29.8 mAP on COCO's test-dev):

Installation

• Clone this repository and enter it:

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

• Set up the environment using one of the following methods:
  • Using Anaconda
    • Run conda env create -f environment.yml
  • Manually with pip
    • Set up a Python3 environment (e.g., using virtenv).
    • 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 
      

• 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
  

• If you want to use YOLACT++, compile deformable convolutional layers (from DCNv2). Make sure you have the latest CUDA toolkit installed from NVidia's Website.

  cd external/DCNv2
  python setup.py build develop
  

Evaluation

Here are our YOLACT models (released on April 5th, 2019) along with their FPS on a Titan Xp and mAP on test-dev. Note: These models were re-uploaded to a huggingface collection, as the original download links expired.

| 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 | 33.5 | 29.8 | yolact_base_54_800000.pth | | 700 | Resnet101-FPN | 23.6 | 31.2 | yolact_im700_54_800000.pth |

YOLACT++ models (released on December 16th, 2019):

| Image Size | Backbone | FPS | mAP | Weights | |:----------:|:-------------:|:----:|:----:|----------------------------------------------------------------------------------------------------------------------| | 550 | Resnet50-FPN | 33.5 | 34.1 | yolact_plus_resnet50_54_800000.pth | | 550 | Resnet101-FPN | 27.3 | 34.6 | yolact_plus_base_54_800000.pth |

To evalute the model, put the corresponding weights file in the ./weights directory and run one of the following commands. The name of each config is everything before the numbers in the file name (e.g., yolact_base for yolact_base_54_800000.pth).

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 0.15.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --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=0.15 --top_k=15 --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=0.15 --top_k=15 --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=0.15 --top_k=15 --images=path/to/input/folder:path/to/output/folder

Video

# Display a video in real-time. "--video_multiframe" will process that many frames at once for improved performance.
# If you want, use "--display_fps" to draw the FPS directly on the frame.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=my_video.mp4

# Display a webcam feed in real-time. If you have multiple webcams pass the index of the webcam you want instead of 0.
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --video=0

# Process a video and save it to another file. This uses the same pipeline as the ones above now, so it's fast!
python eval.py --trained_model=weights/yolact_base_54_800000.pth --score_threshold=0.15 --top_k=15 --video_multiframe=4 --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

By default, we train on COCO. Make sure to download the entire dataset using the commands above.

• 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

Multi-GPU Support

YOLACT now supports multiple GPUs seamlessly during training:

• Before running any of the scripts, run: `export CUDA_VISIB