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Running Experiments

Note: Before running the experiments, ensure you've correctly prepared your datasets and checkpoints. If you haven't done so, please refer back to the Prepare Datasets and Prepare Checkpoints tutorials.

Overview

In this tutorial, we'll guide you through the process of running experiments using the SAM-PT model. We utilize the Hydra framework to manage configurations for our experiments, simplifying the customization of various model components, such as the point tracker, dataset, and model versions.

Running SAM-PT

To run SAM-PT with the default VOS evaluation configuration vos_eval_root.yaml, use the following command:

python -m sam_pt.vos_eval.eval model=sam_pt

Using Different Point Trackers

The flexibility of SAM-PT allows for the usage of different point trackers. To specify a different point tracker, append it to the command. For instance, to use RAFT, TapNet, PIPS, etc., as the point tracker, execute the following:

python -m sam_pt.vos_eval.eval model=sam_pt model/point_tracker=raft

python -m sam_pt.vos_eval.eval model=sam_pt model/point_tracker=tapnet

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/point_tracker=pips \
  model.positive_points_per_mask=8

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/point_tracker=pips_plus_plus \
  model.positive_points_per_mask=8

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/point_tracker=tapir \
  model.positive_points_per_mask=32 \
  model.negative_points_per_mask=72 \
  model.iterative_refinement_iterations=24

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/point_tracker=superglue \
  model.negative_points_per_mask=0 \
  model.positive_point_selection_method=random \
  model.negative_point_selection_method=random \
  model.iterative_refinement_iterations=3

For more point trackers to chose from, see the available ones in configs/model/point_tracker, or implement a new one by implementing the interface defined by sam_pt.point_tracker.tracker.PointTracker.

Using Different Variants of SAM

Use SAM with different ViT backbones with the following commands:

# SAM's ViT-Huge (used throughout the ablation study)
python -m sam_pt.vos_eval.eval model=sam_pt \
  model/[email protected]_predictor.sam_model=sam_vit_huge \
  model.sam_predictor._target_=segment_anything.predictor.SamPredictor

# ViT-Large
python -m sam_pt.vos_eval.eval model=sam_pt \
  model/[email protected]_predictor.sam_model=sam_vit_large \
  model.sam_predictor._target_=segment_anything.predictor.SamPredictor

# ViT-Base
python -m sam_pt.vos_eval.eval model=sam_pt \
  model/[email protected]_predictor.sam_model=sam_vit_base \
  model.sam_predictor._target_=segment_anything.predictor.SamPredictor

Using smaller backbones results in slight inference speed gains, with a slight performance deterioration as measured on the validation subset of DAVIS 2017 (note that the numbers are reported for PIPS as the point tracker):

SAM Variant Backbone J&F FPS
SAM ViT-Huge 76.65 1.4
SAM ViT-Large 76.43 1.8
SAM ViT-Base 72.18 2.6

Replace the SAM model with HQ-SAM or MobileSAM using the following commands:

For HQ-SAM (used by default in configs/model/sam_pt.yaml):

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/[email protected]_predictor.sam_model=samhq_vit_huge \
  model.sam_predictor._target_=segment_anything_hq.predictor.SamPredictor

For Light HQ-SAM:

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/[email protected]_predictor.sam_model=samhq_light_vit_tiny \
  model.sam_predictor._target_=segment_anything_hq.predictor.SamPredictor \
  model.iterative_refinement_iterations=3

For MobileSAM:

python -m sam_pt.vos_eval.eval model=sam_pt \
  model/[email protected]_predictor.sam_model=sam_mobile_vit_tiny \
  model.iterative_refinement_iterations=3

Refer to the comparison table below to understand the differences between each variant with regard to the average $\mathcal{J\&F}$ score on DAVIS 2017 and the inference speed. Note that the comparison is reported with PIPS as the point tracker.

SAM Variant Backbone J&F FPS
SAM ViT-Huge 76.65 1.4
HQ-SAM ViT-Huge 77.64 1.3
Light HQ-SAM ViT-Tiny 71.30 4.8
MobileSAM ViT-Tiny 71.07 5.5

Running VOS Experiments

Evaluate SAM-PT on various VOS datasets like DAVIS 2016, DAVIS 2017, YouTube-VOS 2018, MOSE 2023, and BDD100K using the following commands:

python -m sam_pt.vos_eval.eval model=sam_pt dataset=D16     split=val
python -m sam_pt.vos_eval.eval model=sam_pt dataset=D17     split=val
python -m sam_pt.vos_eval.eval model=sam_pt dataset=D17     split=test
python -m sam_pt.vos_eval.eval model=sam_pt dataset=Y18     split=val
python -m sam_pt.vos_eval.eval model=sam_pt dataset=MOSE    split=val  model.iterative_refinement_iterations=0
python -m sam_pt.vos_eval.eval model=sam_pt dataset=BDD100K split=val  model.iterative_refinement_iterations=0 model.point_tracker_mask_batch_size=100 visualize_results=false

Note that the validation subset of DAVIS 2017 is used for ablation study experiments.

Similarly, you can evaluate SAM-PT-reinit:

python -m sam_pt.vos_eval.eval model=sam_pt_reinit dataset=D17 split=val model.use_point_reinit=true

Running VIS Experiments

To run VIS experiments with SAM-PT, use the following command:

python -m sam_pt.vis_eval.eval [email protected]=sam_pt \
  model.model.iterative_refinement_iterations=0 \
  model.model.add_other_objects_positive_points_as_negative_points=false \
  num_gpus_per_machine=8 num_machines=1 machine_rank=0 dist_url=tcp:https://localhost:27036 \
  DETECTRON2_CONFIG.SEED=36

Please adjust the number of GPUs and machines according to your available hardware setup. If you're experiencing out-of-memory issues, you can reduce the mask batch size using the model.masks_batch_size=10 argument (default value is 100, as shown in vis_eval_root.yaml).

Debugging with a Tiny Dataset

For debugging purposes, you may wish to use a subset of the UVO dataset. A tiny UVO subset can be created and used following the instructions below. We have already registered the uvo_v1_val_tiny split with Detectron2. Should you require the jq command utility, it can be installed without sudo using conda install -c conda-forge jq.

cat data/UVOv1.0/VideoDenseSet/UVO_video_val_dense.json \
  | jq '.videos |= [.[0]] | .annotations |= [.[0,1,2,3]]' \
  > data/UVOv1.0/VideoDenseSet/UVO_video_val_dense.tiny.json

When running the VIS evaluation, include the flag DETECTRON2_CONFIG.DATASETS.TEST=[uvo_v1_val_tiny] to use the tiny dataset.

Running Interactive Point-Based Video Segmentation

To run our simulation of interactive point-based video segmentation with online HQ-SAM-PT on videos 4 and 12 (zero-indexed, correspond to camel and drift-straight videos, respectively) of the DAVIS 2017 validation subset, use the following command:

python -m sam_pt.vos_eval.eval model=sam_pt \
  simulate_interactive_point_correction=true \
  masks_batch_size=1 \
  visualize_results=false \
  vid_ids=\[4,12\] \
  model._target_=sam_pt.modeling.sam_pt_interactive.SamPtInteractive \
  model.iterative_refinement_iterations=0 \
  model.positive_points_per_mask=1 \
  model.negative_points_per_mask=0 \
  +model.visualize_all_interactions_separately=true \
  +model.visualize_all_interactions_as_mp4=true \
  +model.interactions_max=300 \
  +model.interactions_max_per_frame=4 \
  +model.online=true \
  +model.online_interactive_iou_threshold=0.95

Optionally, you can add +model.font_path=/path/to/helvetica.ttf to the command to use a custom font for the text overlay. For videos with a lot of interaction, the visualizations are very time consuming to create and can be disabled by setting the visualization flags to false.

To run the same experiment with point tracking disabled, i.e., to run the baseline that interacts with SAM separately for each frame, use the following command:

python -m sam_pt.vos_eval.eval model=sam_pt \
  simulate_interactive_point_correction=true \
  masks_batch_size=1 \
  visualize_results=false \
  vid_ids=\[4,12\] \
  model._target_=sam_pt.modeling.sam_pt_interactive.SamPtInteractive \
  model.iterative_refinement_iterations=0 \
  +model.visualize_all_interactions_separately=false \
  +model.visualize_all_interactions_as_mp4=true \
  +model.online=true \
  +model.disable_point_tracking=true \
  +model.interactions_max_per_frame=3

Adjust the interactions_max_per_frame parameter to control the number of points/interactions that can be used to prompt SAM per frame.

What's Next?

Now that you've successfully run your experiments with SAM-PT and come to the end of the documentation, there are several possibilities to proceed further:

  1. Analyze the Results: You can analyze both qualitative and quantitative results of your runs on your Weights & Biases (wandb) account.

  2. Integrate a New Point Tracker: If you're interested in exploring new point tracking techniques within SAM-PT, you can integrate a new point tracker by following our API as defined in the PointTracker class. For more details on how to do this, see how RAFT implements the API in the RaftPointTracker class.

  3. Use SAM-PT in Your Own Projects: If you have specific projects or applications where video object segmentation can be beneficial, especially in zero-shot settings with arbitrary objects and segments, SAM-PT can be a useful tool. For instance, you may want to include it in a media editing tool or use it within a broader computer vision pipeline.

  4. Integrate Other VIS/VOS Methods: Our codebase is designed to be flexible. You can integrate other Video Object Segmentation (VOS) or Video Instance Segmentation (VIS) methods by implementing the VOS evaluation API defined by the VOSEvaluator or by Detectron2 for VIS as we did in SamBasedVisToVosAdapter.

  5. Integrate Different SAM Variants: You can also experiment with integrating different SAM variants into our framework. For example, see how we integrated HQ-SAM here or MobileSAM here.

These next steps will allow you to dive deeper into the capabilities of SAM-PT, customize it to your liking, and potentially improve upon its performance. Happy experimenting!