Hugging Face's logo

Datasets:
facebook
/
SACo-Gold

Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
xet
Community
4
SACo-Gold / README.md
hkhedr's picture
hkhedr
initial commit
4c3fefe verified
preview code
|
raw
history blame contribute delete
7.32 kB
metadata
license: other
extra_gated_fields:
  First Name: text
  Last Name: text
  Date of birth: date_picker
  Country: country
  Affiliation: text
  Job title:
    type: select
    options:
      - Student
      - Research Graduate
      - AI researcher
      - AI developer/engineer
      - Reporter
      - Other
  geo: ip_location
  By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: >-
  The information you provide will be collected, stored, processed and shared in
  accordance with the [Meta Privacy
  Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
language:
  - en
configs:
  - config_name: MetaCLIP
    data_files:
      - split: test
        path:
          - gold_metaclip_merged_a_release_test.json
          - gold_metaclip_merged_b_release_test.json
          - gold_metaclip_merged_c_release_test.json
  - config_name: SA-1B
    data_files:
      - split: test
        path:
          - gold_sa1b_merged_a_release_test.json
          - gold_sa1b_merged_b_release_test.json
          - gold_sa1b_merged_c_release_test.json
  - config_name: Attributes
    data_files:
      - split: test
        path:
          - gold_attributes_merged_a_release_test.json
          - gold_attributes_merged_b_release_test.json
          - gold_attributes_merged_c_release_test.json
  - config_name: Crowded
    data_files:
      - split: test
        path:
          - gold_crowded_merged_a_release_test.json
          - gold_crowded_merged_b_release_test.json
          - gold_crowded_merged_c_release_test.json
  - config_name: Wiki-Common1K
    data_files:
      - split: test
        path:
          - gold_wiki_common_merged_a_release_test.json
          - gold_wiki_common_merged_b_release_test.json
          - gold_wiki_common_merged_c_release_test.json
  - config_name: Wiki-Food&Drink
    data_files:
      - split: test
        path:
          - gold_fg_food_merged_a_release_test.json
          - gold_fg_food_merged_b_release_test.json
          - gold_fg_food_merged_c_release_test.json
  - config_name: Wiki-Sports Equipment
    data_files:
      - split: test
        path:
          - gold_fg_sports_equipment_merged_a_release_test.json
          - gold_fg_sports_equipment_merged_b_release_test.json
          - gold_fg_sports_equipment_merged_c_release_test.json

Dataset Card for SA-Co/Gold

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