Uploading Annotations
We understand that developers may already have an annotation pipeline, be it with an outsourced workforce or an open source annotation program that works well with your existing datasets. We allow the users to upload their annotation files to the server to either use Nexus as an annotator for your team or just simply use Nexus for the model training phase.
How Do I Upload Annotations?
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Go to the Annotations tab - You can find the button on the top of the Datasets homepage.
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Select your relevant Import Format - You should select the annotation format that your file was constructed in. Please note that each annotation format has a required file type. Nexus supports a variety of annotation formats and we are striving to constantly cover more formats from different tools. To see more detailed information about the different Annotation Types, look through them in Supported Annotation Formats or locate them in the table below.
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Select the Upload Annotations button - You can drag-and-drop or browse for your existing annotation files for processing.
The images need to be uploaded onto the platform before uploading the annotations, to ensure the platform can accurately match your annotation to the image filenames.
Supported Annotation Formats
Object Detection
While we allow TFRecord for export, we don't accept TFRecord for import.
Annotation data can be presented in a Normalized or Unnormalized format. The system will do its best to infer the corresponding types.
| Annotation Type | Description | Required File Type |
|---|---|---|
| COCO | This file format is typically export from COCO Annotator and LabelMe. The sample expected file format that is accepted is as follows | JSON |
| CSV Four Corners | This is another typical format exported when accessing Kaggle Datasets. The file is presented in a .csv format where individual rows represent 1 annotation each and the headers must be as such | CSV |
| CSV Width Height | Another common CSV based representation of image annotation where the width and height of the bounding box is given | CSV |
| Pascal VOC | The PascalVOC annotation type is commonly exported from LabelImg and should be in an xml filetype. | XML |
| YOLO DarkNet | This annotation format is commonly prepared to train YOLO models and it contains a label file and multiple .txt files for describing each image's annotations. | TXT |
| YOLO Keras PyTorch | This format is almost the same as the YOLO format above, however it allows a single .txt file to describe all the annotations as such | TXT |
| CreateML | This file format is typically exported from CreateML which has items per image. | JSON |
Segmentation
| Annotation Type | Description | Required File Type |
|---|---|---|
| COCO Annotator Polygons / Masks | This uses the same COCO JSON format except the annotations component should be replaced by something like the following example below. | JSON |
| LabelMe Mask / Polygon | This file format is typically exported from LabelMe and provides 1 annotation file per image. Users can upload every individual annotation file that looks as such: | JSON |
Classification
| Annotation Type | Description | Required File Type |
|---|---|---|
| CSV Classification | CSV file that contains two columns, one for images, and one for the labels. | CSV |
Keypoint
| Annotation Type | Description | Required File Type |
|---|---|---|
| COCO Keypoint | Similar to the original COCO format, but instead of having the segmentation vertices, you contain keypoints as a list of arrays. | JSON |
Additionally, we support uploading of predefined skeletons for keypoints. This is to make pre-existing skeletons compatible with our skeleton editor. We currently support skeleton files with these formats:
| Skeleton File Type | Description | Required File Type |
|---|---|---|
| Datature Skeleton | This is our own custom skeleton schema, that integrates anything that would be needed. | JSON |
Format Descriptions and Examples
COCO - Bounding Box
This file format is typically export from COCO Annotator and LabelMe. The sample expected file format that is accepted is as follows
{
"info": {
"year": "2020",
"version": "1",
"description": "Datature.io COCO Format",
"contributor": "",
"url": "https://datature.io/datasets/cat/3",
"date_created": "2020-09-01T01:40:57+00:00"
},
"licenses": [
{
"id": 1,
"url": "",
"name": "Unknown"
}
],
"categories": [
{
"id": 0,
"name": "WBC",
"supercategory": "none"
},
...
],
"images": [
{
"id": 0,
"license": 1,
"file_name": "0.jpg",
"height": 480,
"width": 640,
"date_captured": "2020-09-01T01:53:10+00:00"
},
...
],
"annotations": [
{
"id": 0,
"image_id": 0,
"category_id": 0,
"bbox": [
260,
177,
231,
199
],
"area": 307200,
"segmentation": [],
"iscrowd": 0
},
...
]
}
CSV Four Corner - Bounding Box
This is another typical format exported when accessing Kaggle Datasets. The file is presented in a .csv format where individual rows represent 1 annotation each and the headers must be as such
filename,xmin,ymin,xmax,ymax,label
0.jpg,260,177,491,376,WBC
0.jpg,78,336,184,435,RBC
0.jpg,63,237,169,336,RBC
0.jpg,214,362,320,461,RBC
0.jpg,414,352,506,445,RBC
0.jpg,555,356,640,455,RBC
0.jpg,469,412,567,480,RBC
0.jpg,1,333,87,437,RBC
0.jpg,4,406,95,480,RBC
0.jpg,155,74,247,174,RBC
0.jpg,11,84,104,162,RBC
0.jpg,534,39,639,139,RBC
0.jpg,547,195,640,295,RBC
0.jpg,388,11,481,111,RBC
0.jpg,171,175,264,275,RBC
0.jpg,260,1,374,83,RBC
0.jpg,229,91,343,174,RBC
0.jpg,69,144,184,235,RBC
0.jpg,482,131,594,230,RBC
0.jpg,368,89,464,176,RBC
CSV Width Height - Bounding Box
Another common CSV based representation of image annotation where the width and height of the bounding box is given
filename,xmin,ymin,width,height,label
0.jpg,260,177,231,199,WBC
0.jpg,78,336,106,99,RBC
1.jpg,68,315,218,165,WBC
1.jpg,346,361,100,93,RBC
Pascal VOC - Bounding Box
The PascalVOC annotation type is commonly exported from LabelImg and should be in an xml filetype as such
<annotation>
<folder></folder>
<filename>0.jpg</filename>
<path>0.jpg</path>
<source>
<database>datature.io</database>
</source>
<size>
<width>640</width>
<height>480</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>WBC</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<occluded>0</occluded>
<bndbox>
<xmin>260</xmin>
<xmax>491</xmax>
<ymin>177</ymin>
<ymax>376</ymax>
</bndbox>
</object>
</annotation>
YOLO DarkNet - Bounding Box
Do note that the labels file needs to have the exact filename
label.labelsor the annotation import will throw an error.
This annotation format is commonly prepared to train YOLO models. It contains a label.labels file to describe the classes and multiple <image_name>.txt files for describing each image's annotations. Both files have to be presented as such:
# sample label.labels [Required]
WBC
RBC
# sample image1.txt for image1.jpg
0 0.40625 0.36875 0.3609375 0.414583333333333
1 0.121875 0.7 0.165625 0.20625
YOLO Keras PyTorch - Bounding Box
Do note that the labels file needs to have the exact filename
label.txtor the annotation import will throw an error.
This format is almost the same as the YOLO format above with a label.txt file to describe the classes, however it allows a single .txt file to describe all the annotations. Both files have to be presented as such:
# label.txt [Required]
WBC
RBC
# annotation.txt
0.jpg 260,177,491,376,0
0.jpg 78,336,184,435,1
0.jpg 63,237,169,336,1
0.jpg 214,362,320,461,1
0.jpg 69,144,184,235,1
0.jpg 482,131,594,230,1
0.jpg 368,89,464,176,1
1.jpg 68,315,286,480,0
1.jpg 346,361,446,454,1
1.jpg 53,179,146,299,1
1.jpg 449,400,536,480,1
1.jpg 165,160,257,264,1
1.jpg 464,209,566,319,1
CreateML Bounding Box
This file format is typically exported from CreateML which has items per image.
[
{
"image": "0001.jpg",
"annotations": [
{
"label": "helmet",
"coordinates": {
"x": 162.5,
"y": 45,
"width": 79,
"height": 88
}
},
{
"label": "person",
"coordinates": {
"x": 145.5,
"y": 176,
"width": 251,
"height": 350
}
}
]
}
]
COCO Annotator Polygons / Masks
This format is typically exported by COCO Annotator describes segmentation masks and the photos all in a single .json file as such:
{
"images": [
{
"height": 2048,
"width": 2048,
"id": 0,
"file_name": "X2.5_7_6_-6.0409_-4.2648_-5.5302.jpg"
}
],
"categories": [
{
"supercategory": "Dog",
"id": 0,
"name": "Dog"
},
{
"supercategory": "P",
"id": 1,
"name": "P"
}
],
"annotations": [
{
"segmentation": [
[
1021.2389380530973,
1103.5398230088495,
1027.433628318584,
1176.9911504424776,
813.2743362831858,
1209.7345132743362,
837.1681415929203,
1107.9646017699115
]
],
"iscrowd": 0,
"area": 17280.131568642784,
"image_id": 0,
"bbox": [
813.0,
1104.0,
214.0,
106.0
],
"category_id": 4,
"id": 1
}
]
}
LabelMe Polygon / Mask
This file format is typically exported from LabelMe and provides 1 annotation file per image. Users can upload every individual annotation file that looks as such:
{
"version": "4.5.6",
"flags": {},
"shapes": [
{
"label": "Ox",
"points": [
[
62.576687116564415,
1244.7852760736196
]
],
"group_id": null,
"shape_type": "polygon",
"flags": {}
},
{
"label": "Ox",
"points": [
[
560.483870967742,
1112.0967741935483
]
],
"group_id": null,
"shape_type": "polygon",
"flags": {}
},
{
"label": "P",
"points": [
[
539.516129032258,
1090.3225806451612
]
],
"group_id": null,
"shape_type": "polygon",
"flags": {}
}
],
"imagePath": "X2.5_1_0_-8.3827_-3.7965_-0.3163.jpg",
"imageHeight": 2048,
"imageWidth": 2048
}
CSV Classification
This file format is a simple format for classification labels. It matches image filenames with the class labels by having two columns.
filename,label
image_1.png,cat
image_2.png,dog
COCO Keypoint
COCO Keypoint follows the same format as the other formats, except that it has keypoints additionally. Keypoints are of the format (x1,y1,v1,x2,y2,v2,...,xk,yk,vk), for k as the total number of keypoints. xk,yk refer to the x and y coordinates of the keypoint, and vk refers to the visibility of the keypoint, where 0 is not labelled, in which case x=y=0, 1 meaning labelled but not visible, and 2 as labelled and visible.
{
"images": [
{
"height": 2048,
"width": 2048,
"id": 0,
"file_name": "X2.5_7_6_-6.0409_-4.2648_-5.5302.jpg"
}
],
"categories": [
{
"supercategory": "Dog",
"id": 0,
"name": "Dog",
"keypoints": [
"nose",
"eye",
"ear",
...
],
"skeleton": [
[1, 2],
[2, 3],
[1, 3],
...
]
}
],
"annotations": [
{
"keypoints": [
612.4,
503.7,
2,
752.8,
296.5,
2,
...
],
"image_id": 0,
"category_id": 4,
"id": 1,
"num_keypoints": 17,
...
}
]
}
Datature Skeleton File
Skeleton file encodes the skeleton incorporating other complex parameters that can affect skeletons, such as chirality, camera angle and position, and skeleton information like labels.
{
"info": [
{
"description": "Marcus' Skeletons",
"version": "1.0",
"year": 2023,
"contributor": "Marcus",
"date_created": "2023/09/05",
"url": "nexus.datature.io"
},
{"...": "..."}
],
"licenses": [
{
"url": "datature.io",
"name": "Marcus Skeleton 1"
}
],
"cameras": [
{
"name": "Leftmost Camera",
"parameters": {
"focalLength": [<fx>, <fy>], // in pixels
"principalPoint": [<cx>, <cy>, <cz>], // in pixels
"skew": 0.0,
"radialDistortion"?: [0.0, 0.0],
"tangentialDistortion"?: [0.0, 0.0]
}
}
],
"chirality": [
{
"name": "Custom Transform 1",
"order": {
"groups": [
["Head"],
["Chest"],
["Left Hand", "Right Hand"],
["Left Leg", "Right Leg"],
["First Toe", "Second Toe", "Third Toe", "Fourth Toe", "Fifth Toe"]
],
"perform": "rotate" // or "flip"
},
"affects": "<List of Skeleton Names/Ids>",
"transform": {
"minTranslate": [0.0, 0.0, 0.0],
"maxTranslate": [0.0, 0.0, 0.0],
"minRotate": [0.0, 0.0, 0.0],
"maxRotate": [0.0, 0.0, 0.0],
"minScale": [0.0, 0.0, 0.0],
"maxScale": [0.0, 0.0, 0.0],
"minProjection": [0.0, 0.0, 0.0],
"maxProjection": [0.0, 0.0, 0.0]
}
}
],
"skeletons": [
{
"name": "Skeleton 1",
"keypoints": [
{
"name": "Right Leg",
"category": ["Leg", "Limb", "Right"], // Characteristics
"kwargs"? : ... // Additional parameters for customisability
},
{
"name": "Left Leg",
"category": ["Leg", "Limb", "Left"],
"kwargs"? : ...
}
],
"connections": [
{
"pair": ["Right Leg", "Left Leg"], // or
"pair": [0, 1],
"kwargs"? : ...
}
]
}
],
}
Updated 5 months ago