segment-anything-2/tools/README.md

SAM 2 toolkits

This directory provides toolkits for additional SAM 2 use cases.

Semi-supervised VOS inference

The vos_inference.py script can be used to generate predictions for semi-supervised video object segmentation (VOS) evaluation on datasets such as DAVIS, MOSE or the SA-V dataset.

After installing SAM 2 and its dependencies, it can be used as follows (DAVIS 2017 dataset as an example). This script saves the prediction PNG files to the --output_mask_dir.

python ./tools/vos_inference.py \
  --sam2_cfg sam2_hiera_b+.yaml \
  --sam2_checkpoint ./checkpoints/sam2_hiera_base_plus.pt \
  --base_video_dir /path-to-davis-2017/JPEGImages/480p \
  --input_mask_dir /path-to-davis-2017/Annotations/480p \
  --video_list_file /path-to-davis-2017/ImageSets/2017/val.txt \
  --output_mask_dir ./outputs/davis_2017_pred_pngs

(replace /path-to-davis-2017 with the path to DAVIS 2017 dataset)

To evaluate on the SA-V dataset with per-object PNG files for the object masks, we need to add the --per_obj_png_file flag as follows (using SA-V val as an example). This script will also save per-object PNG files for the output masks under the --per_obj_png_file flag.

python ./tools/vos_inference.py \
  --sam2_cfg sam2_hiera_b+.yaml \
  --sam2_checkpoint ./checkpoints/sam2_hiera_base_plus.pt \
  --base_video_dir /path-to-sav-val/JPEGImages_24fps \
  --input_mask_dir /path-to-sav-val/Annotations_6fps \
  --video_list_file /path-to-sav-val/sav_val.txt \
  --per_obj_png_file \
  --output_mask_dir ./outputs/sav_val_pred_pngs

(replace /path-to-sav-val with the path to SA-V val)

Then, we can use the evaluation tools or servers for each dataset to get the performance of the prediction PNG files above.

Note: a limitation of the vos_inference.py script above is that currently it only supports VOS datasets where all objects to track already appear on frame 0 in each video (and therefore it doesn't apply to some datasets such as LVOS that have objects only appearing in the middle of a video).