---
license: apache-2.0
datasets:
- MCG-NJU/Tracking-Any-Granularity
library_name: transformers
---

🏠 [Homepage](https://tracking-any-granularity.github.io/) | 📄 [Paper](https://arxiv.org/abs/2510.18822) | 🔗 [GitHub](https://github.com/MCG-NJU/SAM2-Plus)

Model repository for SAM 2++: Tracking Anything at Any Granularity, a unified video tracking framework that extends the SAM 2 model to track any targets in videos at any granularity, including masks, bounding boxes, and points.
See the [SAM 2++ paper](https://arxiv.org/abs/2510.18822) for more information.

## Usage

**[Video Object Segmentation (Mask Granularity)](https://github.com/MCG-NJU/SAM2-Plus/blob/main/tools/vos_inference_plus.sh)**
```
import os
import torch
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
from natsort import natsorted

from sam2_plus.build_sam import build_sam2_video_predictor_plus

from tools.visualization import show_mask, show_box, show_points
from tools.vos_inference import load_ann_png, get_per_obj_mask, DAVIS_PALETTE, save_masks_to_dir

predictor = build_sam2_video_predictor_plus(
    config_file="configs/sam2.1/sam2.1_hiera_b+_predmasks_decoupled_MAME.yaml",
    ckpt_path="./checkpoints/SAM2-Plus/checkpoint_phase123.pt",
    apply_postprocessing=False,
    hydra_overrides_extra=[
        "++model.non_overlap_masks=" + ("false")
    ],
    vos_optimized=False,
    task='mask'
)

input_video_dir = "./examples/JPEGImages/horsejump-low"
input_mask_path = "./examples/Annotations/horsejump-low/00000.png"
output_mask_dir = "./output/Annotations/"

score_thresh = 0

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    inference_state = predictor.init_state(video_path=input_video_dir)

    video_name = os.path.basename(input_video_dir)
    frame_names = [
        os.path.splitext(p)[0]
        for p in os.listdir(input_video_dir)
        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG", ".png", ".PNG"]
    ]
    frame_names = natsorted(frame_names)
    height = inference_state["video_height"]
    width = inference_state["video_width"]

    input_frame_idx = 0     # the frame index we interact with
    object_id = 1           # give a unique id to each object we interact with (it can be any integers)

    input_palette = None
    input_mask, input_palette = load_ann_png(input_mask_path)
    per_obj_input_mask = get_per_obj_mask(input_mask)
    object_mask = per_obj_input_mask[object_id]

    predictor.add_new_mask(
        inference_state=inference_state,
        frame_idx=input_frame_idx,
        obj_id=object_id,
        mask=object_mask,
    )

    # run propagation throughout the video and collect the results in a dict
    os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
    output_palette = input_palette or DAVIS_PALETTE
    video_segments = {}  # video_segments contains the per-frame segmentation results
    for out_frame_idx, out_obj_ids, out_mask_logits, _, _ in predictor.propagate_in_video(
        inference_state
    ):
        per_obj_output_mask = {
            out_obj_id: (out_mask_logits[i] > score_thresh).cpu().numpy()
            for i, out_obj_id in enumerate(out_obj_ids)
        }
        video_segments[out_frame_idx] = per_obj_output_mask
    
    # write the output masks as palette PNG files to output_mask_dir
    for out_frame_idx, per_obj_output_mask in video_segments.items():
        save_masks_to_dir(
            output_mask_dir=output_mask_dir,
            video_name=video_name,
            frame_name=frame_names[out_frame_idx],
            per_obj_output_mask=per_obj_output_mask,
            height=height,
            width=width,
            per_obj_png_file=False,
            output_palette=output_palette,
        )
    
    # visualize the tracking results
    for out_frame_idx in tqdm(range(0, len(frame_names)), desc="Visualization Results"):
        plt.clf()
        plt.figure()
        # plt.title(f"frame {out_frame_idx}")
        plt.imshow(Image.open(os.path.join(input_video_dir, frame_names[out_frame_idx] + ".jpg")))
        for out_obj_id, out_mask in video_segments[out_frame_idx].items():
            show_mask(out_mask, plt.gca(), obj_id=out_obj_id)
        plt.axis('off')
        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
        plt.savefig(f"{output_mask_dir}/{video_name}/{out_frame_idx:05d}_withMask.png", dpi=300, bbox_inches='tight', pad_inches=0)
        plt.close()
```

**[Video Object Tracking (Box Granularity)](https://github.com/MCG-NJU/SAM2-Plus/blob/main/tools/sot_inference_plus.sh)**
```
import os
import torch
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
from natsort import natsorted
import numpy as np
import logging

from sam2_plus.build_sam import build_sam2_video_predictor_plus

from tools.visualization import show_mask, show_box, show_points
from tools.vos_inference import load_ann_png, get_per_obj_mask, DAVIS_PALETTE, save_masks_to_dir
from tools.sot_inference import save_boxes_to_dir, save_masks_and_boxes_to_dir
from training.dataset_plus.box.utils import np_box_xywh_to_xyxy, np_box_xyxy_to_xywh, np_masks_to_boxes, np_box_clamp_xywh
from benchmarks.sot_benchmark.datasets.utils import load_text

predictor = build_sam2_video_predictor_plus(
    config_file="configs/sam2.1/sam2.1_hiera_b+_predmasks_decoupled_MAME.yaml",
    ckpt_path="./checkpoints/SAM2-Plus/checkpoint_phase123.pt",
    apply_postprocessing=False,
    hydra_overrides_extra=[
        "++model.non_overlap_masks=" + ("false")
    ],
    vos_optimized=False,
    task='box'
)

input_video_dir = "./examples/JPEGImages/horsejump-low"
input_box_path = "./examples/Boxes/horsejump-low.txt"
output_box_dir = "./output/Boxes/"

score_thresh = 0

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    inference_state = predictor.init_state(video_path=input_video_dir)

    video_name = os.path.basename(input_video_dir)
    frame_names = [
        os.path.splitext(p)[0]
        for p in os.listdir(input_video_dir)
        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG", ".png", ".PNG"]
    ]
    frame_names = natsorted(frame_names)
    height = inference_state["video_height"]
    width = inference_state["video_width"]

    input_frame_idx = 0     # the frame index we interact with
    object_id = 1           # give a unique id to each object we interact with (it can be any integers)

    input_palette = None
    if os.path.isfile(input_box_path):
        input_box_xywh = load_text(str(input_box_path), delimiter=',', dtype=np.float64, backend='numpy').reshape(-1, 4)[0]
    else:
        print(f"Box file {input_box_path} not found. Using default box.")
        input_box_xywh = [316,385,742,488]
    per_obj_input_box_xyxy = {1: np_box_xywh_to_xyxy(np.array(input_box_xywh))}
    object_box_xyxy = per_obj_input_box_xyxy[object_id]

    frame_idx, obj_ids, masks, _ = predictor.add_new_points_or_box(
        inference_state=inference_state,
        frame_idx=input_frame_idx,
        obj_id=object_id,
        box=object_box_xyxy,
    )

    # run propagation throughout the video and collect the results in a dict
    output_palette = input_palette or DAVIS_PALETTE
    video_segments = {}  # video_segments contains the per-frame segmentation results
    video_boxes_xywh = {}  # video_boxes_xyxy contains the per-frame bounding box results
    for out_frame_idx, out_obj_ids, out_mask_logits, output_box_xyxy, out_obj_score_logits in predictor.propagate_in_video(
        inference_state=inference_state,
    ):
        if torch.any(output_box_xyxy[:,:,0] >= output_box_xyxy[:,:,2]) or torch.any(output_box_xyxy[:,:,1] >= output_box_xyxy[:,:,3]):
            logging.warning(f"Invalid box prediction: {output_box_xyxy}")
    
        per_obj_output_mask = {
            out_obj_id: (out_mask_logits[i] > score_thresh).cpu().numpy()
            for i, out_obj_id in enumerate(out_obj_ids)
        }
        video_segments[out_frame_idx] = per_obj_output_mask
        per_obj_output_box_xywh = {
            out_obj_id: np_box_clamp_xywh(np_box_xyxy_to_xywh(output_box_xyxy[i].cpu().numpy()))
            for i, out_obj_id in enumerate(out_obj_ids)
        }
        video_boxes_xywh[out_frame_idx] = per_obj_output_box_xywh
    
    # save the tracking results
    save_boxes_to_dir(
        output_bbox_dir=output_box_dir,
        video_name=video_name,
        video_boxes_xywh=video_boxes_xywh,
    )
    
    # visualize the tracking results
    os.makedirs(os.path.join(output_box_dir, video_name), exist_ok=True)
    for out_frame_idx in tqdm(range(0, len(frame_names)), desc="Visualization Results"):
        plt.clf()
        plt.figure()
        # plt.title(f"frame {out_frame_idx}")
        plt.imshow(Image.open(os.path.join(input_video_dir, frame_names[out_frame_idx] + ".jpg")))
        for out_obj_id, out_box in video_boxes_xywh[out_frame_idx].items():
            box_xywh = out_box[0]
            box_xyxy = np_box_xywh_to_xyxy(np.array(box_xywh))
            show_box(box_xyxy, plt.gca())
        plt.axis('off')
        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
        plt.savefig(os.path.join(output_box_dir, video_name, f"{out_frame_idx:05d}_withbox.png"), dpi=300, bbox_inches='tight', pad_inches=0)
        plt.close()
```

**[Point Tracking (Point Granularity)](https://github.com/MCG-NJU/SAM2-Plus/blob/main/tools/pt_inference_plus.sh)**
```
import os
import torch
import matplotlib.pyplot as plt
from PIL import Image
from tqdm import tqdm
import numpy as np
from natsort import natsorted

from sam2_plus.build_sam import build_sam2_video_predictor_plus

from tools.visualization import show_mask, show_box, show_points
from tools.vos_inference import load_ann_png, get_per_obj_mask, DAVIS_PALETTE, save_masks_to_dir
from tools.pt_inference_plus import load_visible_points_from_npz

predictor = build_sam2_video_predictor_plus(
    config_file="configs/sam2.1/sam2.1_hiera_b+_predmasks_decoupled_MAME.yaml",
    ckpt_path="./checkpoints/SAM2-Plus/checkpoint_phase123.pt",
    apply_postprocessing=False,
    hydra_overrides_extra=[
        "++model.non_overlap_masks=" + ("false")
    ],
    vos_optimized=False,
    task='point'
)

input_video_dir = "./examples/JPEGImages/horsejump-low"
input_point_path = "./examples/Points/horsejump-low.npz"
output_point_dir = "./output/Points/"

radius, sigma = 5, 2
score_thresh = 0

with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
    video_name = os.path.basename(input_video_dir)
    frame_names = [
        os.path.splitext(p)[0]
        for p in os.listdir(input_video_dir)
        if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG", ".png", ".PNG"]
    ]
    frame_names = natsorted(frame_names)

    inference_state = predictor.init_state(video_path=input_video_dir)
    height = inference_state["video_height"]
    width = inference_state["video_width"]

    input_frame_idx = 0     # the frame index we interact with
    object_id = 0           # give a unique id to each object we interact with (it can be any integers)
    num_frames, num_points = len(frame_names), 1

    input_data = np.load(input_point_path, allow_pickle=True)
    input_point, input_visible = torch.tensor(input_data['trajs_2d'].astype(np.float32)), torch.tensor(input_data['visibs'].astype(bool))
    per_obj_input_point = load_visible_points_from_npz(
        input_points=input_point,
        input_visibles=input_visible,
        frame_idx=input_frame_idx,
    )
    object_point = per_obj_input_point[object_id]

    predictor.add_new_points_and_generate_gaussian_mask(
        inference_state=inference_state,
        frame_idx=input_frame_idx,
        obj_id=object_id,
        points=object_point.unsqueeze(0).numpy(),
        labels=np.array([1]),
        radius=radius,
        sigma=sigma,
    )

    # run propagation throughout the video and collect the results in a dict
    point_array = -np.ones((num_frames, num_points, 2), dtype=np.float32)
    visible_array = np.zeros((num_frames, num_points), dtype=bool)
    for out_frame_idx, out_obj_ids, out_mask_logits, out_box_xyxys, out_obj_score_logits in predictor.propagate_in_video(
        inference_state
    ):
        for out_obj_id, out_mask_logit, out_obj_score_logit in zip(out_obj_ids, out_mask_logits, out_obj_score_logits):
            out_mask_logit, out_obj_score_logit = out_mask_logit.squeeze(0), out_obj_score_logit.squeeze(0)
            max_index = torch.argmax(out_mask_logit)
            max_score_y, max_score_x = torch.unravel_index(max_index, out_mask_logit.shape)
            point_array[out_frame_idx, out_obj_id] = np.array([max_score_x.cpu(), max_score_y.cpu()])
            visible_array[out_frame_idx, out_obj_id] = (out_obj_score_logit > score_thresh).cpu().numpy()

    # write the output masks as palette PNG files to output_mask_dir
    os.makedirs(output_point_dir, exist_ok=True)
    np.savez(os.path.join(output_point_dir, f"{video_name}.npz"), trajs_2d=point_array, visibs=visible_array, size=(width, height))

    # visualize the tracking results
    os.makedirs(os.path.join(output_point_dir, video_name), exist_ok=True)
    for out_frame_idx in tqdm(range(0, len(frame_names)), desc="Visualization Results"):
        plt.clf()
        plt.figure()
        # plt.title(f"frame {out_frame_idx}")
        plt.imshow(Image.open(os.path.join(input_video_dir, frame_names[out_frame_idx] + ".jpg")))
        points = point_array[out_frame_idx, object_id].reshape(1, 2)
        labels = np.array([-1], np.int32)
        show_points(points, labels, plt.gca(), marker_size=20, edgecolor=None)
        plt.axis('off')
        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)
        plt.savefig(os.path.join(output_point_dir, video_name, f"{out_frame_idx:05d}_withPoint.png"), dpi=300, bbox_inches='tight', pad_inches=0)
        plt.close()
```

### Load from 🤗 Hugging Face

Models can alternatively be loaded from [Hugging Face](https://huggingface.co/MCG-NJU/SAM2-Plus)

```
import torch
from sam2_plus.sam2_video_predictor import SAM2VideoPredictor_Plus

predictor = SAM2VideoPredictor_Plus.from_pretrained("MCG-NJU/SAM2-Plus")
```