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tools/README.md

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+## 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](https://davischallenge.org/index.html), [MOSE](https://henghuiding.github.io/MOSE/) or the SA-V dataset.
+
+After installing SAM 2 and its dependencies, it can be used as follows ([DAVIS 2017 dataset](https://davischallenge.org/davis2017/code.html) as an example). This script saves the prediction PNG files to the `--output_mask_dir`.
+```bash
+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.
+```bash
+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](https://lingyihongfd.github.io/lvos.github.io/) that have objects only appearing in the middle of a video).