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EVALUATION.md

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Inference and Evaluation

We provide:

  1. Pretrained Cutie model: https://github.com/hkchengrex/Cutie/releases/tag/v1.0 or https://drive.google.com/drive/folders/1E9ESHFlGU2KQkeRfH14kZzbdnwA0dH0f?usp=share_link
  2. Pre-computed outputs: https://drive.google.com/drive/folders/1x-jf5GHB4hypU9cDZ0VSkMKGm8MR0eQJ?usp=share_link

Note: the provided BURST visualizations were not done correctly. You can use scripts/convert_burst_to_vos_train.py to visualize from the prediction JSON instead.

Preparation

  1. Datasets should be placed out-of-source. See TRAINING.md for the directory structure.

  2. For the LVOS validation set, pre-process it by keeping only the first annotations:

python scripts/data/preprocess_lvos.py ../LVOS/valid/Annotations ../LVOS/valid/Annotations_first_only
  1. Download the models that you need and place them in ./output.

Video Object Segmentation

python cutie/eval_vos.py dataset=[dataset] weights=[path to model file] model=[small/base]
  • Possible options for dataset: see cutie/config/eval_config.yaml.
  • To test on your own data, use dataset=generic and directly specify the image/mask directories. Example:
python cutie/eval_vos.py dataset=generic image_directory=examples/images mask_directory=examples/masks size=480
  • To separate outputs from different models/settings, specify exp_id=[some unique id].
  • By default, the results are saved out-of-source in ../output/.
  • By default, we only use the first-frame annotation in the generic mode. Specify --use_all_masks to incorporate new objects (as in the YouTubeVOS dataset).
  • To evaluate with the "plus" setting, specify --config-name eval_plus_config.yaml immediately after python cutie/eval_vos.py before other arguments.

To get quantitative results:

Understanding Palette, Or, What is Going on with the Colored PNGs?

See https://github.com/hkchengrex/XMem/blob/main/docs/PALETTE.md.