This page describes the dataset formats for computer-vision apps supported by TAO Toolkit.
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
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.
NoteThe 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.jsonfile 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"]
}
Since TAO Toolkit 3.0-22.05, all object detection models support COCO format. Using the COCO format requires data to be organized in this structure:
annotation{
"id": int,
"image_id": int,
"category_id": int,
"bbox": [x,y,width,height],
"area": float,
"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": [{"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"}]
See the COCO website for a description of the COCO format. A COCO dataset preparation script, which automatically downloads and converts the dataset to TFRecords, is provided in the TAO container. In the EfficientDet notebook, you can run the script as follows:
download_and_preprocess_coco.sh $data_dir
When using a custom dataset, you should follow the COCO format closely and convert the dataset to TFRecords using the following command (refer to L68-75 in download_and_preprocess_coco.sh for more detail).
python create_coco_tf_record.py
--logtostderr
--train_image_dir=$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
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"}]
See the COCO website for a description of the COCO format. A COCO dataset preparation script, which automatically downloads and converts the dataset to TFRecords, is provided in the TAO container. 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
This section describes the dataset formats supported by UNet/ Segformer for loading images and masks.
If you have the masks saved in COCO format, refer to :ref: sample_usage_of_the_dataset_converter_tool_unet<Dataset Converter> to convert COCO format to UNet mask image format.
Semantic Segmentation Mask Format
This section describes the format of the mask images for different types of input_image_type or input_type.
Refer dataset_config_unet for more information on configuring the input_image_type for UNet.
Refer dataset_config_segformer for more information on configuring the input_type for Segformer.
Color/ RGB Input Image Type
For the color/ rgb input images, each mask image is a single-channel or three-channel image with size
equal to the input image. Every pixel in the mask should have an integer value that represents the
segmentation class label_id, as per the mapping provided in the dataset_config_unet and dataset_config_segformer.
Ensure that the value of the pixels in the mask image are within the range of the label_id
values provided in the dataset_config and dataset_config_segformer.
For a reference example, refer to the _labelIds.png images format in the Cityscapes Dataset.
Grayscale Input Image Type
For grayscale input images, the mask is a single channel image with size equal to the input image.
Every pixel has a value of 255 or 0, which corresponds respectively to a label_id of 1 or 0
in the dataset_config and dataset_config_segformer. For reference, refer to ISBI dataset Jupyter
notebook example provided in ngcresources.
Image and Mask Loading Format
Segformer
For SegFormer, the path to images and mask folders can directly be provided in the dataset_config_segformer. Please ensure that the image and the corresponding mask names are same. The image and mask extension need not be necessarily same.
UNet
Structured Images and Masks Folders for UNet
The data folder structure for images and masks must be in the following format for UNet:
/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
testfolder in the above directory structure is optional; any folder can be used for inference.
Image and Mask Text files for UNet
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.
The size of the images need not necessarily be equal to the model input dimensions. The images are resized internally to model input dimensions.
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 contains the labels under results.
The bounding box and gesture class are separate entries with 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.
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, 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 structure:
.
{
"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:
filenamefield: specifies the path to the images to train on.classfield: category of the labels for the respective section.annotationfield: annotation chunk.
There are three supported chunk in the annotation including FaceBbox, FiducialPoints, and eyes.
FaceBoxchunk: 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 |
|
Version of the labeling tool for this chunk |
Float |
N/A |
|
|
Occlusion state [ 0 = not occlused, 1 = occluded ] |
Integer |
0 or 1 |
|
|
x coordinate of top left corner of outer face bounding box |
Float |
[0, image_width] |
|
|
y coordinate of top left corner of outer face bounding box |
Float |
[0, image_height] |
|
|
Width of the outer face bounding box |
Float |
[0, image_width] |
|
|
Height of the outer face bounding box |
Float |
[0, image_height] |
|
|
x coordinate of top left corner of tight face bounding box |
Float |
[0, image_width] |
|
|
y coordinate of top left corner of outer face bounding box |
Float |
[0, image_height] |
|
|
Width of the outer face bounding box |
Float |
[0, image_width] |
|
|
Height of the outer face bounding box |
Float |
[0, image_height] |
|
FiducialPointchunk: 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 |
|
Version of the labeling tool for this chunk |
Float |
N/A |
|
|
Occlusion status [ 0 = not occlused, 1 = occluded ] |
Integer |
0 or 1 |
|
|
x coordinate of the ith landmarks point |
Float |
[0, image_width] |
|
|
y coordinate of the ith landmarks point |
Float |
[0, image_height] |
|
|
Width of the outer face bounding box |
String |
N/A |
|
eyeschunk: 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 |
|
Version of the labeling tool for this chunk |
Float |
N/A |
|
|
x coordinate of left eye center |
Float |
[0, image_width] |
|
|
y coordinate of left eye center |
Float |
[0, image_height] |
|
|
x coordinate of right eye center |
Float |
[0, image_width] |
|
|
y coordinate of right eye center |
Float |
[0, image_height] |
|
|
Status of the left eye |
String |
open/close/barely open/half open/occluded |
|
|
Status of the right eye |
String |
open/close/barely open/half open/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,
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}
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
imagessection contains the complete list of images in the dataset with some metadata.
Image IDs need to be unique among other images.
Parameter name |
Description |
Type |
Range |
|---|---|---|---|
|
The path to the image |
String |
N/A |
|
The height of the image |
Integer |
N/A |
|
The width of the image |
Float |
N/A |
|
The unique ID of the image |
Integer |
N/A |
The
annotationssection contains the labels for the images. Each entity is one annotation, and each image can have multiple annotations.
Parameter name |
Description |
Type |
Range |
|---|---|---|---|
|
A list of polygons, which has a list of vertices for a given person/group. |
List |
N/A |
|
The number of keypoints that are labeled |
Integer |
[0, total_keypoints] |
|
The area of the segmentation/bbox |
Float |
N/A |
|
If 1, indicates that the annotation mask is for multiple people |
Integer |
[0, 1] |
|
A list of keypoints with the following format: |
List |
N/A |
|
The bbox of the object/person |
List |
N/A |
|
The unique ID of the associated image |
Integer |
N/A |
|
The object category (always |
Integer |
1 |
|
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
categoriessection contains the keypoint convention that is followed in the dataset
Parameter name |
Description |
Type |
Range |
|---|---|---|---|
|
The supercategory |
String |
person |
|
The ID of the category |
Integer |
1 |
|
The name of the category |
String |
person |
|
The keypoint names and ordering convention as used in labeling |
List |
N/A |
|
A list of skeleton edges with the following format: |
List |
N/A |
For more details, please refer to the COCO keypoint annotations file and COCO Keypoint Detection Task.