README.md

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language:
- en
license: other
pretty_name: SACo-VEval
configs:
- config_name: SACo-VEval SA-V
  data_files:
  - split: test
    path: annotation/saco_veval_sav_test.json
  - split: val
    path: annotation/saco_veval_sav_val.json
- config_name: SACo-VEval YT-Temporal-1B
  data_files:
  - split: test
    path: annotation/saco_veval_yt1b_test.json
  - split: val
    path: annotation/saco_veval_yt1b_val.json
- config_name: SACo-VEval SmartGlasses
  data_files:
  - split: test
    path: annotation/saco_veval_smartglasses_test.json
  - split: val
    path: annotation/saco_veval_smartglasses_val.json
---

# SA-Co/VEval Dataset
**License** each domain has its own License
* SA-Co/VEval - SA-V: CC-BY-NC 4.0
* SA-Co/VEval - YT-Temporal-1B: CC-BY-NC 4.0
* SA-Co/VEval - SmartGlasses: CC-by-4.0
  
**SA-Co/VEval** is an evaluation dataset comprising of 3 domains, each domain has a val and test split.
* SA-Co/VEval - SA-V: videos are from the [SA-V dataset](https://ai.meta.com/datasets/segment-anything-video/)
* SA-Co/VEval - YT-Temporal-1B: videos are from the [YT-Temporal-1B](https://cove.thecvf.com/datasets/704)
* SA-Co/VEval - SmartGlasses: egocentric videos from [Smart Glasses](https://huggingface.co/datasets/facebook/SACo-VEval/blob/main/media/saco_sg.tar.gz)

This Hugging Face dataset repo contains the following contents:
```
datasets/facebook/SACo-VEval/tree/main/
├── annotation/
│   ├── saco_veval_sav_test.json
│   ├── saco_veval_sav_val.json
│   ├── saco_veval_smartglasses_test.json
│   ├── saco_veval_smartglasses_val.json
│   ├── saco_veval_yt1b_test.json
│   ├── saco_veval_yt1b_val.json
└── media/
    ├── saco_sg.tar.gz
    └── yt1b_start_end_time.json
```
* annotation
  * all the GT json files
* media
  * `saco_sg.tar.gz`: the preprocessed JPEGImages for SA-Co/VEval - SmartGlasses
  * `yt1b_start_end_time.json`: the Youtube video ids and the start and end time used in SA-Co/VEval - YT-Temporal-1B

More detail to prepare the complete SA-Co/VEval Dataset can be found in the [SAM 3 Github](https://github.com/facebookresearch/sam3/tree/main/scripts/eval/veval).

## Annotation Format
The format is similar to the [YTVIS](https://youtube-vos.org/dataset/vis/) format.

In the annotation json, e.g. `saco_veval_sav_test.json` there are 5 fields:
* info:
    * A dict containing the dataset info
    * E.g. {'version': 'v1', 'date': '2025-09-24', 'description': 'SA-Co/VEval SA-V Test'}
* videos
    * A list of videos that are used in the current annotation json
    * It contains {id, video_name, file_names, height, width, length}
* annotations
    * A list of **positive** masklets and their related info
    * It contains {id, segmentations, bboxes, areas, iscrowd, video_id, height, width, category_id, noun_phrase}
        * video_id should match to the `videos - id` field above
        * category_id should match to the `categories - id` field below
        * segmentations is a list of [RLE](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py)
* categories
    * A **globally** used noun phrase id map, which is true across all 3 domains.
    * It contains {id, name}
        * name is the noun phrase
* video_np_pairs
    * A list of video-np pairs, including both **positive** and **negative** used in the current annotation json
    * It contains {id, video_id, category_id, noun_phrase, num_masklets}
        * video_id should match the `videos - id` above
        * category_id should match the `categories - id` above
        * when `num_masklets > 0` it is a positive video-np pair, and the presenting masklets can be found in the annotations field
        * when `num_masklets = 0` it is a negative video-np pair, meaning no masklet presenting at all
```
data {
    "info": info
    "videos": [video]
    "annotations": [annotation]
    "categories": [category]
    "video_np_pairs": [video_np_pair]
}
video {
    "id": int
    "video_name": str  # e.g. sav_000000
    "file_names": List[str]
    "height": int
    "width": width
    "length": length
}
annotation {
    "id": int
    "segmentations": List[RLE]
    "bboxes": List[List[int, int, int, int]]
    "areas": List[int]
    "iscrowd": int
    "video_id": str
    "height": int
    "width": int
    "category_id": int
    "noun_phrase": str
}
category {
    "id": int
    "name": str
}
video_np_pair {
    "id": int
    "video_id": str
    "category_id": int
    "noun_phrase": str
    "num_masklets" int
}
```
SAM 3 Github [sam3/examples/saco_veval_vis_example.ipynb](https://github.com/facebookresearch/sam3/blob/main/examples/saco_veval_vis_example.ipynb) shows some examples of the data format and data visualization.