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GETTING_STARTED.md

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Getting Started with DVIS

This document provides a brief intro of the usage of DVIS.

Please see Getting Started with Detectron2 for full usage.

Training

We provide a script train_net_video.py, that is made to train all the configs provided in DVIS.

To train a model with "train_net_video.py", first setup the corresponding datasets following datasets/README.md, then download the pre-trained weights of MinVIS from here or minvis_model_zoo and put them in the current working directory. Once these are set up, run:

# train the DVIS_Online
python train_net_video.py \
  --num-gpus 8 \
  --config-file /path/to/DVIS_Online_config_file.yaml \
  --resume MODEL.WEIGHTS /path/to/minvis_pretrained_weights.pth

# train the DVIS_Offline
python train_net_video.py \
  --num-gpus 8 \
  --config-file /path/to/DVIS_Offline_config_file.yaml \
  --resume MODEL.WEIGHTS /path/to/DVIS_Online_weights.pth

Training on a new dataset

If you want to train on a new dataset, first need to fine-tune the segmenter according to the MinVIS process. Please download the COCO (Instance/Panoptic/Semantic) pre-trained weights of Mask2Former from here and put them in the current working directory. Once these are set up, run:

# finetune the segmenter
python train_net_video.py \
  --num-gpus 8 \
  --config-file /path/to/MinVIS_config_file.yaml \
  --resume MODEL.WEIGHTS /path/to/Mask2Former_COCO_pretrained_weights.pth

# train the DVIS_Online
python train_net_video.py \
  --num-gpus 8 \
  --config-file /path/to/DVIS_Online_config_file.yaml \
  --resume MODEL.WEIGHTS /path/to/MinVIS_weights.pth

# train the DVIS_Offline
python train_net_video.py \
  --num-gpus 8 \
  --config-file /path/to/DVIS_Offline_config_file.yaml \
  --resume MODEL.WEIGHTS /path/to/DVIS_Online_weights.pth

Evaluation

Prepare the datasets following datasets/README.md and download trained weights from here. Once these are set up, run:

python train_net_video.py \
  --num-gpus 8 \
  --config-file /path/to/config.yaml \
  --eval-only MODEL.WEIGHTS /path/to/weight.pth

Visualization

  1. Pick a trained model and its config file. To start, you can pick from model zoo, for example, configs/ovis/DVIS_Online_R50.yaml.
  2. We provide demo_long_video.py to visualize outputs of a trained model. Run it with:
python demo_long_video.py \
  --config-file /path/to/config.yaml \
  --input /path/to/images_folder \
  --output /path/to/output_folder \  
  --opts MODEL.WEIGHTS /path/to/checkpoint_file.pth

# if the video if long (> 300 frames), plese set the 'windows_size'
python demo_long_video.py \
  --config-file /path/to/config.yaml \
  --input /path/to/images_folder \
  --output /path/to/output_folder \  
  --windows_size 300 \
  --opts MODEL.WEIGHTS /path/to/checkpoint_file.pth

The input is a folder containing video frames saved as images. For example, ytvis_2019/valid/JPEGImages/00f88c4f0a.