Data Annotation Format

This page describes the dataset formats for computer-vision apps supported by TAO Toolkit.

Image Classification Format

Image classification expects a directory of images with the following structure, where each class has its own directory with the class name. The naming convention for train/val/test can be different because the path of each set is individually specified in the spec file. See the Specification File for Classification section for more information.

|--dataset_root:
    |--train
        |--audi:
            |--1.jpg
            |--2.jpg
        |--bmw:
            |--01.jpg
            |--02.jpg
    |--val
        |--audi:
            |--3.jpg
            |--4.jpg
        |--bmw:
            |--03.jpg
            |--04.jpg
    |--test
        |--audi:
            |--5.jpg
            |--6.jpg
        |--bmw:
            |--05.jpg
            |--06.jpg

Object Detection – KITTI Format

Using the KITTI format requires data to be organized in this structure:

.
|--dataset root
  |-- images
      |-- 000000.jpg
      |-- 000001.jpg
            .
            .
      |-- xxxxxx.jpg
  |-- labels
      |-- 000000.txt
      |-- 000001.txt
            .
            .
      |-- xxxxxx.txt
  |-- kitti_seq_to_map.json

Here’s a description of the structure:

  • The images directory contains the images to train on.

  • The labels directory contains the labels to the corresponding images. Details of this file are included in the Label Files section.

    Note

    The images and labels have the same file IDs before the extension. The image to label correspondence is maintained using this file name.

  • The kitti_seq_to_map.json file contains a sequence to frame ID mapping for the frames in the images directory. This is an optional file and is useful if the data needs to be split into N folds sequence wise. In case the data is to be split into a random 80:20 train:val split, then this file may be ignored.

Label Files

A KITTI format label file is a simple text file containing one line per object. Each line has multiple fields. Here is a description of these fields:

Num elements

Parameter name

Description

Type

Range

Example

1

Class names

The class to which the object belongs.

String

N/A

Person, car, Road_Sign

1

Truncation

How much of the object has left image boundaries.

Float

0.0, 0.1

0.0

1

Occlusion

Occlusion state [ 0 = fully visible, 1 = partly visible, 2 = largely occluded, 3 = unknown].

Integer

[0,3]

2

1

Alpha

Observation Angle of object

Float

[-pi, pi]

0.146

4

Bounding box coordinates: [xmin, ymin, xmax, ymax]

Location of the object in the image

Float(0 based index)

[0 to image width],[0 to image_height], [top_left, image_width], [bottom_right, image_height]

100 120 180 160

3

3-D dimension

Height, width, length of the object (in meters)

Float

N/A

1.65, 1.67, 3.64

3

Location

3-D object location x, y, z in camera coordinates (in meters)

Float

N/A

-0.65,1.71, 46.7

1

Rotation_y

Rotation ry around the Y-axis in camera coordinates

Float

[-pi, pi]

-1.59

The sum of the total number of elements per object is 15. Here is a sample text file:

car 0.00 0 -1.58 587.01 173.33 614.12 200.12 1.65 1.67 3.64 -0.65 1.71 46.70 -1.59
cyclist 0.00 0 -2.46 665.45 160.00 717.93 217.99 1.72 0.47 1.65 2.45 1.35 22.10 -2.35
pedestrian 0.00 2 0.21 423.17 173.67 433.17 224.03 1.60 0.38 0.30 -5.87 1.63 23.11 -0.03

This indicates that in the image there are 3 objects with parameters as mentioned above. Currently, for detection the toolkit only requires the class name and bbox coordinates fields to be populated. This is because the TAO training pipe supports training only for class and bbox coordinates. The remaining fields may be set to 0. Here is a sample file for a custom annotated dataset:

car 0.00 0 0.00 587.01 173.33 614.12 200.12 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cyclist 0.00 0 0.00 665.45 160.00 717.93 217.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00
pedestrian 0.00 0 0.00 423.17 173.67 433.17 224.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Sequence Mapping File

This is an optional JSON file that captures the mapping between the frames in the images directory and the names of video sequences from which these frames were extracted. This information is needed while doing an N-fold split of the dataset. This way frames from one sequence don’t repeat in other folds and one of the folds could be used for validation. Here’s an example of the json dictionary file.

{
  "video_sequence_name": [list of strings(frame idx)]
}

Here’s an example of a kitti_seq_to_frames.json file with a sample dataset with six sequences:

{
  "2011_09_28_drive_0165_sync": ["003193", "003185", "002857", "001864", "003838",
  "007320", "003476", "007308", "000337", "004165", "006573"],
  "2011_09_28_drive_0191_sync": ["005724", "002529", "004136", "005746"],
  "2011_09_28_drive_0179_sync": ["005107", "002485", "006089", "000695"],
  "2011_09_26_drive_0079_sync": ["005421", "000673", "002064", "000783", "003068"],
  "2011_09_28_drive_0035_sync": ["005540", "002424", "004949", "004996", "003969"],
  "2011_09_28_drive_0117_sync": ["007150", "003797", "002554", "001509"]
}

Instance Segmentation – COCO format

Using the COCO format requires data to be organized in this structure:

annotation{
"id": int,
"image_id": int,
"category_id": int,
"segmentation": RLE or [polygon],
"area": float,
"bbox": [x,y,width,height],
"iscrowd": 0 or 1,
}

image{
"id": int,
"width": int,
"height": int,
"file_name": str,
"license": int,
"flickr_url": str,
"coco_url": str,
"date_captured": datetime,
}

categories[{
"id": int,
"name": str,
"supercategory": str,
}]

An example COCO annotation file is shown below:

"annotations": [{"segmentation": [[510.66,423.01,511.72,420.03,510.45,416.0,510.34,413.02,510.77,410.26,510.77,407.5,510.34,405.16,511.51,402.83,511.41,400.49,510.24,398.16,509.39,397.31,504.61,399.22,502.17,399.64,500.89,401.66,500.47,402.08,499.09,401.87,495.79,401.98,490.59,401.77,488.79,401.77,485.39,398.58,483.9,397.31,481.56,396.35,478.48,395.93,476.68,396.03,475.4,396.77,473.92,398.79,473.28,399.96,473.49,401.87,474.56,403.47,473.07,405.59,473.39,407.71,476.68,409.41,479.23,409.73,481.56,410.69,480.4,411.85,481.35,414.93,479.86,418.65,477.32,420.03,476.04,422.58,479.02,422.58,480.29,423.01,483.79,419.93,486.66,416.21,490.06,415.57,492.18,416.85,491.65,420.24,492.82,422.9,493.56,424.39,496.43,424.6,498.02,423.01,498.13,421.31,497.07,420.03,497.07,415.15,496.33,414.51,501.1,411.96,502.06,411.32,503.02,415.04,503.33,418.12,501.1,420.24,498.98,421.63,500.47,424.39,505.03,423.32,506.2,421.31,507.69,419.5,506.31,423.32,510.03,423.01,510.45,423.01]],"area": 702.1057499999998,"iscrowd": 0,"image_id": 289343,"bbox": [473.07,395.93,38.65,28.67],"category_id": 18,"id": 1768}],
"images": [{"license": 1,"file_name": "000000407646.jpg","coco_url": "http://images.cocodataset.org/val2017/000000407646.jpg","height": 400,"width": 500,"date_captured": "2013-11-23 03:58:53","flickr_url": "http://farm4.staticflickr.com/3110/2855627782_17b93a684e_z.jpg","id": 407646}],
"categories": [{"supercategory": "person","id": 1,"name": "person"},{"supercategory": "vehicle","id": 2,"name": "bicycle"},{"supercategory": "vehicle","id": 3,"name": "car"},{"supercategory": "vehicle","id": 4,"name": "motorcycle"}]

For more details, please check COCO format. A COCO dataset preparation script is provided in the TAO container which automatically downloads and converts the dataset to TFRecords. In the MaskRCNN notebook, you can run the script as follows:

download_and_preprocess_coco.sh $data_dir

When using a custom dataset, you should follow the COCO format closely and convert the dataset to TFRecords using the following command (refer to L68-75 in download_and_preprocess_coco.sh for more detail).

python create_coco_tf_record.py
  --logtostderr
  --include_masks
  --train_image_dir=$TRAIN_IMAGE_DIR
  --val_image_dir=$VAL_IMAGE_DIR
  --train_object_annotations_file=$TRAIN_COCO_ANNOTATION_FILE
  --val_object_annotations_file=$VAL_ANNOTATION_FILE
  --output_dir=$OUTPUT_DIR

Semantic Segmentation – UNet Format

This section describes the formats of the dataset for training a semantic segmentation UNet in TAO, namely:

  • Stuctured Image and Masks Folder

  • Image and Masks Text File

Structured Images and Masks Folders

UNet expects the images and corresponding masks encoded as images. Each mask image is a single-channel image, where every pixel is assigned an integer value that represents the segmentation class. The data folder structure for images and masks must be in the following format:

/Dataset_01
    /images
      /train
        0000.png
        0001.png
        ...
        ...
        N.png
      /val
        0000.png
        0001.png
        ...
        ...
        N.png
      /test
        0000.png
        0001.png
        ...
        ...
        N.png
    /masks
      /train
        0000.png
        0001.png
        ...
        ...
        N.png
      /val
        0000.png
        0001.png
        ...
        ...
        N.png
  • See the Folders based Dataset Config section for further details about configuring these image and mask folder paths in experiment spec.

  • Each image and label has the same file ID before the extension. The image-to-label correspondence is maintained using this filename. The test folder in the above directory structure is optional; any folder can be used for inference.

Image and Mask Text files

An image text file containing the paths to all the images and a mask text file containing the paths to the corresponding mask files. The image names and mask names should full abosolute unix paths.

Contents of example images text file images_source1.txt is shown below:

/home/user/workspace/exports/images_final/00001.jpg
/home/user/workspace/exports/images_final/00002.jpg

Contents of example corresponding mask text file labels_source1.txt is shown below. It contains the corresponding mask names:

/home/user/workspace/exports/masks_final/00001.png
/home/user/workspace/exports/masks_final/00002.png
  • Text file method additionally allows to specify multiple sequences.

  • These text file paths should be provided in spec file.

See Text files based Dataset Config section for further details about configuring multiple data sources using text files in dataset config.

Note

The size of the images need not necessarily be equal to the model input dimensions. The images are resized internally to model input dimensions.

Gesture Recognition – Custom Format

A gesture recognition model should perform well on users outside the training dataset. Thus, model training requires user segregation when splitting into train, validation and test dataset. To enable this we need some unique identifier, user_id for each subject. In addition each subject might record multiple videos.

We wish to organise dataset in the following format:

.
|-- original dataset root
  |-- uid_1
      |-- session_1
          |-- 000000.png
          |-- 000001.png
                .
                .
          |-- xxxxxx.png
      |-- session_2
          |-- 000000.png
          |-- 000001.png
                .
                .
          |-- xxxxxx.png
  |-- uid_2
      |-- session_1
          |-- 000000.png
          |-- 000001.png
                .
                .
          |-- xxxxxx.png
      |-- session_2
          |-- 000000.png
          |-- 000001.png
                .
                .
          |-- xxxxxx.png
  |-- uid_3
      |-- session_1
          |-- 000000.png
          |-- 000001.png
                .
                .
          |-- xxxxxx.png

For each set we also prepare a metadata file that captures fields that can be used for dataset sampling.

{
    "set": "data",
    "users": {
        "uid_1": {
            "location": "outdoor",
            "illumination": "good",
            "class_fps": {
                "session_1": 30,
                "session_2": 30
            }
        },
        "uid_2": {
            "location": "indoor",
            "illumination": "good",
            "class_fps": {
                "session_1": 10,
                "session_2": 15
            }
        },
        "uid_3": {
            "location": "indoor",
            "illumination": "poor",
            "class_fps": {
                "session_1": 10
            }
        }
    }
}

Label Format

Each image corresponds to a subject performing a gesture. The image requires a corresponding label JSON which contains a bounding box for the hand of interest and gesture label. We follow the Label Studio format. A sample label for an image is:

{
  "completions": [
    {
      "result": [
        {
          "type": "rectanglelabels",
          "original_width": 320,
          "original_height": 240,
          "value": {
            "x": 58.1,
            "y": 18.3,
            "width": 18.8,
            "height": 49.5
          }
        },
        {
          "type": "choices",
          "value": {
            "choices": [
              "Thumbs-up"
            ]
          }
        }
      ]
    }
  ],
  "task_path": "/workspace/tao-experiments/gesturenet/data/uid_1/session_1/image_0001.png"
}
  • task_path: specifies the full path to the image.

  • completions: This is a chunk that conatins the labels under results.

The bounding box and gesture class are seperate entries whith the following type

  • rectanglelabels: specifies the label corresponding to hand bounding box.

Parameter name

Description

Type

Range

type

The type of label

String

rectanglelabels

original_width

Width of image being labelled (in pixels)

Integer

[1, inf)

original_height

Height of image being labelled (in pixels)

Integer

[1, inf)

value[“x”]

x coordinate of top left corner of hand bounding box (as a percentage of image width)

Float

[0, 100]

value[“y”]

y coordinate of top left corner of hand bounding box (as a percentage of image height)

Float

[0, 100]

value[“width”]

Width of the hand bounding box (as a percentage of image width)

Float

[0, 100]

value[“height”]

Height of the hand bounding box (as a percentage of image height)

Float

[0, 100]

  • choices: specifies the label corresponding to gesture class.

Parameter name

Description

Type

Range

type

The type of label

String

choices

value[“choices”]

List of attributes. For GestureNet app this will be a single entry with gesture class name

List of strings

Valid gesture classes

The dataset_convert tool requires an extraction and experiment configuration spec files input. The details of the configuration files and sample usage examples are included on the Gesture Recognition page.

Heart Rate Estimation – Custom Format

HeartRateNet expects directories of images in the format shown below. The images and ground truth labels are then converted to TFRecords for training.

Subject_001/
    ground_truth.csv
    image_timestamps.csv
    images/
        0000.bmp
        0001.bmp
            .
            .
        N.bmp
.
.
Subject_M/
    ground_truth.csv
    image_timestamps.csv
    images/
        0000.bmp
        0001.bmp
            .
            .
        Y.bmp

EmotionNet, FPENET, GazeNet – JSON Label Data Format

EmotionNet, FPENet, and GazeNet use the same JSON data format labeled by the NVIDIA data factory team. These apps expect data in this Json data format for training and evaluation. For EmotionNet, FPENet, and GazeNet, this data is converted to TFRecords for training. TFRecords help iterate faster through the data. Please refer to the corresponding section for the JSON data format descriptions.

Using the Json Label data format requires data to be organized in a json file with the following struture:

.
{
     "filename": "data/001_01_02_200_06.png",
     "class": "image",
     "annotations": [
         {
             "class": "FaceBbox",
             "tool-version": "1.0",
             "Occlusion": 0,
             "face_outer_bboxx": 269.0082935424086,
             "face_outer_bboxy": 44.33839032556304,
             "face_outer_bboxwidth": 182.97858097042064,
             "face_outer_bboxheight": 276.28773076003836,
             "face_tight_bboxx": 269.211755426433,
             "face_tight_bboxy": 147.9049289218409,
             "face_tight_bboxwidth": 182.58110482105968,
             "face_tight_bboxheight": 172.5088694283426
         },
         {
             "class": "FiducialPoints",
             "tool-version": "1.0",
             "P1x": 304.8502837500011,
             "P1y": 217.10946645000078,
             "P2x": 311.0173699500011,
             "P2y": 237.15249660000086,
             .
             .
             "P26occluded": true,
             "P46occluded": true,
             .
             .
             "P68x": 419.5885050000024,
             "P68y": 267.6976650000015,
             .
             .
             "P104x": 429.6,
             "P104y": 189.5,
         },
         {
             "class": "eyes"
             "tool-version": "1.0",
             "l_eyex": 389.1221901922325,
             "l_eyey": 197.94528259092206,
             "r_eyex": 633.489814294182,
             "r_eyey": 10.52527209626886,
             "l_status": "open",
             "r_status": "occluded",
         }
     ]
 }

Here’s a description of the structure:

  • filename field: specifies the path to the images to train on.

  • class field: category of the labels for the respective section.

  • annotation field: annotation chunk.

There are three supported chunk in the annotation including FaceBbox, FiducialPoints, and eyes.

  • FaceBox chunk: This is a chunk that describe Face Bounding Box labeling information.

Parameter name

Description

Type

Range

Example

class

The class for the annotation chunk

String

N/A

FaceBbox

tool-version

Version of the labeling tool for this chunk

Float

N/A

1.0

Occlusion

Occlusion state [ 0 = not occlused, 1 = occluded ]

Integer

0 or 1

0

face_outer_bboxx

x coordinate of top left corner of outer face bounding box

Float

[0, image_width]

269.05

face_outer_bboxy

y coordinate of top left corner of outer face bounding box

Float

[0, image_height]

44.33

face_outer_bboxwidth

Width of the outer face bounding box

Float

[0, image_width]

182.97

face_outer_bboxheight

Height of the outer face bounding box

Float

[0, image_height]

276.28

face_tight_bboxx

x coordinate of top left corner of tight face bounding box

Float

[0, image_width]

269.21

face_tight_bboxy

y coordinate of top left corner of outer face bounding box

Float

[0, image_height]

147.90

face_tight_bboxwidth

Width of the outer face bounding box

Float

[0, image_width]

182.58

face_tight_bboxheight

Height of the outer face bounding box

Float

[0, image_height]

172.50

  • FiducialPoint chunk: This is a chunk that describes Fiducial Point Labeling information.

Parameter name

Description

Type

Range

Example

class

The class for the annotation chunk

String

N/A

FaceBbox

tool-version

Version of the labeling tool for this chunk

Float

N/A

1.0

Occlusion

Occlusion status [ 0 = not occlused, 1 = occluded ]

Integer

0 or 1

0

Pix

x coordinate of the ith landmarks point

Float

[0, image_width]

304.85

Piy

y coordinate of the ith landmarks point

Float

[0, image_height]

217.10

Pioccluded

Width of the outer face bounding box

String

N/A

true

  • eyes chunk: This is a chunk that describes eyes labeling information. This chunk is not required.

Parameter name

Description

Type

Range

Example

class

The class for the annotation chunk

String

N/A

FaceBbox

tool-version

Version of the labeling tool for this chunk

Float

N/A

1.0

l_eyex

x coordinate of left eye center

Float

[0, image_width]

389.12

l_eyey

y coordinate of left eye center

Float

[0, image_height]

197.94

r_eyex

x coordinate of right eye center

Float

[0, image_width]

633.48

r_eyey

y coordinate of right eye center

Float

[0, image_height]

182.97

l_status

Status of the left eye

String

open/close/barely open/half open/occluded

open

r_status

Status of the right eye

String

open/close/barely open/half open/occluded

occluded

Here’s an example of a json file with a sample dataset with two image frames:

[
    {
        "filename": "data/001_01_02_200_06.png",
        "class": "image",
        "annotations": [
            {
                "face_outer_bboxy": 44.33839032556304,
                "face_outer_bboxx": 269.0082935424086,
                "face_tight_bboxx": 269.211755426433,
                "face_tight_bboxy": 147.9049289218409,
                "tool-version": "1.0",
                "face_tight_bboxwidth": 182.58110482105968,
                "face_tight_bboxheight": 172.5088694283426,
                "face_outer_bboxwidth": 182.97858097042064,
                "Occlusionx": 0,
                "class": "FaceBbox",
                "face_outer_bboxheight": 276.28773076003836
            },
            {
                "P91x": 395.3500000000004,
                "P91y": 196.6500000000002,
                "P74occluded": true,
                "P28x": 436.44144340908053,
                "P28y": 174.67157210032852,
                "P52y": 252.53100000000143,
                "P52x": 428.9925000000024,
                "P32y": 236.48449500000103,
                "P32x": 416.6063550000018,
                "P44x": 427.65443026467267,
                "P44y": 186.9615161604129,
                "P99x": 425.75,
                "P36occluded": true,
                "P75x": 428.85,
                "P75y": 190.95000000000002,
                "P20x": 389.46879000000166,
                "P20y": 178.13376000000076,
                "P8y": 313.8318038340011,
                "P8x": 407.70466707150143,
                "P81y": 192.2500000000002,
                "P94x": 427.70000000000005,
                "P81x": 393.5500000000004,
                "P12y": 268.179948238501,
                "P12x": 408.69280247400155,
                "P65y": 260.04348000000147,
                "P65x": 429.0319800000024,
                "P84x": 396.8500000000004,
                "P84y": 194.4500000000002,
                "P93occluded": true,
                "P46occluded": true,
                "P43y": 193.31428917697824,
                "P43x": 421.12354211680173,
                "P14occluded": true,
                "P92y": 187.5,
                "P54occluded": true,
                "P53x": 433.50450000000245,
                "P53y": 251.9670000000014,
                "P45occluded": true,
                "P33x": 426.3480450000019,
                "P33y": 238.67140500000104,
                "P60x": 413.82301500000233,
                "P100occluded": true,
                "P60y": 272.07148500000153,
                "P23y": 174.7903155211989,
                "P23x": 428.12940394815394,
                "P90y": 194.9000000000002,
                "P13x": 399.2067026100015,
                "P13y": 257.903340052501,
                "P7x": 388.1395861020014,
                "P7y": 304.93858521150105,
                "P61y": 262.1309850000015,
                "P104x": 429.6,
                "P104y": 189.5,
                "P83y": 193.2500000000002,
                "P83x": 395.0000000000004,
                "P61x": 404.5783500000023,
                "P50y": 254.6756100000014,
                "P50x": 414.2206350000023,
                "P100x": 424.8,
                "P100y": 191.3,
                "P34y": 240.46069500000107,
                "P34x": 435.9903300000019,
                "P18y": 188.2730700000008,
                "P18x": 366.50623500000154,
                "P25occluded": true,
                "P102occluded": true,
                "P46x": 436.0852131464696,
                "P46y": 191.82999641609848,
                "P58y": 275.0536350000016,
                "P58x": 429.2307900000024,
                "P77x": 306.5418228495726,
                "P77y": 258.61884245799524,
                "P97occluded": true,
                "P99y": 192.9,
                "P10y": 293.87146870350114,
                "P10x": 434.97720418050164,
                "P48occluded": true,
                "P26x": 436.0258414360342,
                "P26y": 171.99984513074497,
                "version": "v1",
                "P27occluded": true,
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BodyposeNet – COCO Format

Using the COCO format requires data to be organized in this structure:

|--dataset root
    |-- train2017
        |-- 000000001000.jpg
        |-- 000000001001.jpg
            .
            .
        |-- xxxxxxxxxxxx.jpg
    |-- val2017
        |-- 000000002000.jpg
        |-- 000000002001.jpg
            .
            .
        |-- xxxxxxxxxxxx.jpg
    |-- annotations
        |-- person_keypoints_train2017.json
        |-- person_keypoints_val2017.json

As long as you have a dataset root, and the filenames are adjusted accordingly in the images->filename field in annotations, you can choose to have a nested directory structure for the train and test images.

Label Files

This section outlines the COCO annotations dataset format that the data must be in for BodyposeNet. Although COCO annotations have more fields, only the attributes that are needed by BodyposeNet are mentioned here. You may use the exact same format as COCO. The dataset should use the following overall structure (in a .json file):

"images": [
    {
        "file_name": "000000001000.jpg",
        "height": 480,
        "width": 640,
        "id": 1000
    },
    {
        "file_name": "000000580197.jpg",
        "height": 480,
        "width": 640,
        "id": 580197
    },
    ...
],
"annotations": [
    {
        "segmentation": [[162.46,152.13,150.73,...173.92,156.23]],
        "num_keypoints": 17,
        "area": 8720.28915,
        "iscrowd": 0,
        "keypoints": [162,174,2,...,149,352,2],
        "image_id": 1000,
        "bbox": [115.16,152.13,83.23,228.41],
        "category_id": 1,
        "id": 1234574
    },
    ...
],
"categories": [
    {
        "supercategory": "person",
        "id": 1,
        "name": "person",
        "keypoints": [
            "nose","left_eye","right_eye","left_ear","right_ear",
            "left_shoulder","right_shoulder","left_elbow","right_elbow",
            "left_wrist","right_wrist","left_hip","right_hip",
            "left_knee","right_knee","left_ankle","right_ankle"
        ],
        "skeleton": [
            [16,14],[14,12],[17,15],[15,13],[12,13],[6,12],[7,13],[6,7],
            [6,8],[7,9],[8,10],[9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]
        ]
    }
]
  • The images section contains the complete list of images in the dataset with some metadata.

Note

Image IDs need to be unique among other images.

Parameter name

Description

Type

Range

file_name

The path to the image

String

N/A

height

The height of the image

Integer

N/A

width

The width of the image

Float

N/A

id

The unique ID of the image

Integer

N/A

  • The annotations section contains the labels for the images. Each entity is one annotation, and each image can have multiple annotations.

Parameter name

Description

Type

Range

segmentation

A list of polygons, which has a list of vertices for a given person/group.

List

N/A

num_keypoints

The number of keypoints that are labeled

Integer

[0, total_keypoints]

area

The area of the segmentation/bbox

Float

N/A

iscrowd

If 1, indicates that the annotation mask is for multiple people

Integer

[0, 1]

keypoints

A list of keypoints with the following format: [x1, y1, v1, x2, y2, v2 ...], where x and y are pixel locations, and v is the visibility/occlusion flag.

List

N/A

bbox

The bbox of the object/person

List

N/A

image_id

The unique ID of the associated image

Integer

N/A

category_id

The object category (always 1 for person)

Integer

1

id

The unique ID of the annotation

Integer

N/A

  • The COCO dataset follows the following occlusion flag labeling format: [visible: 2, occluded: 1, not_labeled: 0]

  • The categories section contains the keypoint convention that is followed in the dataset

Parameter name

Description

Type

Range

supercategory

The supercategory

String

person

id

The ID of the category

Integer

1

name

The name of the category

String

person

keypoints

The keypoint names and ordering convention as used in labeling

List

N/A

skeleton

A list of skeleton edges with the following format: [[j1, j2], [j2, j3] ...], where j is the keypoint/joint index.

List

N/A

For more details, please refer to the COCO keypoint annotations file and COCO Keypoint Detection Task.