SA-Co/Gold is a benchmark for promptable concept segmentation (PCS) in images. The benchmark contains images paired with text labels (also referred as Noun Phrases aka NPs), each annotated exhaustively with masks on all object instances that match the label. SA-Co/Gold comprises 7 subsets, each targeting a different annotation domain. For each subset, the annotations are multi-reviewed and agreed by 3 human annotators resulting in a high-quality benchmark.

This dataset covers 2 image sources and 7 annotation domains. The image sources are: MetaCLIP and SA-1B. The annotation domains are: MetaCLIP captioner NPs, SA-1B captioner NPs, Attributes, Crowded Scenes, Wiki-Common1K, Wiki-Food/Drink, Wiki-Sports Equipment.

More details on the usage SA-Co/Gold dataset including visualization and evaluation can be found in the SAM 3 GitHub.

Annotation Format

The annotation format is derived from COCO format. Notable data fields are:

images: a list of dict features, contains a list of all image-NP pairs. Each entry is related to an image-NP pair and has the following items.
- id: a string feature, unique identifier for the image-NP pair
- text_input: a string feature, the noun phrase for the image-NP pair
- file_name: a string feature, the relative image path in the corresponding data folder.
annotations: a list of dict features, containing a list of all annotations including bounding box, segmentation mask, area etc.
- image_id: a string feature, maps to the identifier for the image-np pair in images
- bbox: a list of float features, containing bounding box in [x,y,w,h] format
- segmentation: a dict feature, containing segmentation mask in RLE format
categories: a list of dict features, containing a list of all categories. Here, we provide the category key for compatibility with the COCO format, but in open-vocabulary detection we do not use it. Instead, the text prompt is stored directly in each image (text_input in images). Note that in our setting, a unique image (id in images) actually corresponds to an (image, text prompt) combination.

For id in images that have corresponding annotations (i.e. exist as image_id in annotations), we refer to them as a "positive" NP. And, for id in images that don't have any annotations (i.e. they do not exist as image_id in annotations), we refer to them as a "negative" NP.

A sample annotation from Wiki-Food/Drink domain looks as follows:

images

[
  {
    "id": 10000000,
    "file_name": "1/1001/metaclip_1_1001_c122868928880ae52b33fae1.jpeg",
    "text_input": "chili",
    "width": 600,
    "height": 600,
    "queried_category": "0",
    "is_instance_exhaustive": 1,
    "is_pixel_exhaustive": 1
  },
  {
    "id": 10000001,
    "file_name": "1/1001/metaclip_1_1001_c122868928880ae52b33fae1.jpeg",
    "text_input": "the fish ball",
    "width": 600,
    "height": 600,
    "queried_category": "2001",
    "is_instance_exhaustive": 1,
    "is_pixel_exhaustive": 1
  }
]

annotations

[
  {
    "id": 1,
    "image_id": 10000000,
    "source": "manual",
    "area": 0.002477777777777778,
    "bbox": [
      0.44333332777023315,
      0.0,
      0.10833333432674408,
      0.05833333358168602
    ],
    "segmentation": {
      "counts": "`kk42fb01O1O1O1O001O1O1O001O1O00001O1O001O001O0000000000O1001000O010O02O001N10001N0100000O10O1000O10O010O100O1O1O1O1O0000001O0O2O1N2N2Nobm4",
      "size": [
        600,
        600
      ]
    },
    "category_id": 1,
    "iscrowd": 0
  },
  {
    "id": 2,
    "image_id": 10000000,
    "source": "manual",
    "area": 0.001275,
    "bbox": [
      0.5116666555404663,
      0.5716666579246521,
      0.061666667461395264,
      0.036666665226221085
    ],
    "segmentation": {
      "counts": "aWd51db05M1O2N100O1O1O1O1O1O010O100O10O10O010O010O01O100O100O1O00100O1O100O1O2MZee4",
      "size": [
        600,
        600
      ]
    },
    "category_id": 1,
    "iscrowd": 0
  }
]

Data Stats

Here are the stats for the 7 annotation domains. The # Image-NPs represent the total number of unique image-NP pairs including both “positive” and “negative” NPs.

Domain	Media	# Image-NPs	# Image-NP-Masks
MetaCLIP captioner NPs	MetaCLIP	33393	20144
SA-1B captioner NPs	SA-1B	13258	30306
Attributes	MetaCLIP	9245	3663
Crowded Scenes	MetaCLIP	20687	50417
Wiki-Common1K	MetaCLIP	65502	6448
Wiki-Food&Drink	MetaCLIP	13951	9825
Wiki-Sports Equipment	MetaCLIP	12166	5075

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