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 configs/sam2.1/sam2.1_hiera_b+.yaml \
--sam2_checkpoint ./checkpoints/sam2.1_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 configs/sam2.1/sam2.1_hiera_b+.yaml \
--sam2_checkpoint ./checkpoints/sam2.1_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: by default, the vos_inference.py script above assumes that all objects to track already appear on frame 0 in each video (as is the case in DAVIS, MOSE or SA-V). For VOS datasets that don't have all objects to track appearing in the first frame (such as LVOS or YouTube-VOS), please add the --track_object_appearing_later_in_video flag when using vos_inference.py.