Data labeling automated with 10 annotation
LABELING AI is a deep learning-based Auto-Labeling technology that utilizes AI to automate and quickly label large amounts of data, after labeling only a small amount of data manually.
LABELING AI is a deep learning-based Auto-Labeling technology that utilizes AI to automate and quickly label large amounts of data, after labeling only a small amount of data manually.
Auto-Labeling is performed with artificial intelligence and a large amount of data can be labeled only by inspecting the results.
Deep learning-based Auto-Labeling can be performed with only 10 labels, and a large amount of data can be labeled only by inspecting and correcting the result of Auto-Labeling.
Manually generate 10 labeled data.
Train an auto labeling AI with the 10 pre-labeled data. Review and correct the results to enhance auto labeling performance.
Repeat the previous step to generate 1,000, 10,000, or 100,000 auto labeled data. Transform your auto labeling AI into an object detection AI model to perform object detection as needed.
Labeling inspection and modification are also easily performed through the magic tool that can quickly label even complex-shaped objects.
We provide a variety of labeling tools such as Bounding Box, Polyline, and Polygon magic tool.
It recognizes and classifies the entire image by region, and is actively used in autonomous driving technology.
It recognizes and tracks human motion by detecting the skeleton, and is used in motion detection fields such as sports or security.
Regardless of the labeling work environment, various coordinate data can be imported to LABELING AI to perform labeling, and the processed labeling coordinate data can be extracted in various formats(COCO JSON / VOC).
{
"images": [
{
"id": "60a212aac869a1fea276480d",
"file_name": "/images/img_labelingExample.jpg",
"width": 4000,
"height": 2084
}
],
"type": "instances",
"annotations": [
{
"segmentation": [
[
1200,
907,
1200,
1882,
2903,
1882,
2903,
907
]
],
"area": 1660425,
"iscrowd": 0,
"ignore": 0,
"image_id": "60a212aac869a1fea276480d",
"bbox": [
1200,
907,
1703,
975
],
"category_id": 2621,
"id": "60a216ae2cd9eb1bbde44e2b"
}
],
"categories": [
{
"supercategory": "none",
"id": 2620,
"name": "bus"
},
{
"supercategory": "none",
"id": 2621,
"name": "car"
},
{
"supercategory": "none",
"id": 2622,
"name": "truck"
}
]
}
<annotation>
<folder>VOC2007</folder>
<filename>img_labelingExample.jpg</filename>
<source>
<database>The VOC2007 Database</database>
<annotation>PASCAL VOC2007</annotation>
<image>none</image>
<flickrid>none</flickrid>
</source>
<owner>
<flickrid>none</flickrid>
<name>[email protected]</name>
</owner>
<size>
<width>4000</width>
<height>2084</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>car</name>
<pose>Unspecified</pose>
<truncated>0</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1201</xmin>
<ymin>908</ymin>
<xmax>2903</xmax>
<ymax>1882</ymax>
</bndbox>
</object>
</annotation>
The entire process from training data preparation to AI deployment can be solved with LABELING AI, saving the cost and duration of the project.
Artificial intelligence development cost
Labeling inspection cost
Manual labeling cost
Labeling tool cost
* Only the auto-labeling results
that passes the inspection
will be processed for payment.
Starting with the first 10 manual labeling, the performance of LABELING AI is further improved by repeating the inspection/correction and re-training of Auto-Labeling results.
Check and manage work status at a glance through the dashboard for the labeling project.
LABELING AI supports various methods to perform Auto-Labeling.
from ds2 import DS2
ds2 = DS2(apptoken=“s2234k3b4”)
ds2.startAutoLabeling(
"people.zip",
"1000",
has_label_data=True,
ai_type="general",
autolabeling_type="box",
general_ai_type="person",
model_id=None,
custom_ai_stage=0,
workapp="object_detection"
)
Build a data pipeline for MLOps at DS2.ai with Python. SDK gives you access to all of the processes from uploading and labeling data, to training and deploying the artificial intelligence model.
Learn more →Starting with the first 10 manual labeling, the performance of LABELING AI is further improved by repeating the inspection/correction and re-training of Auto-Labeling results.
Check and manage work status at a glance through the dashboard for the labeling project.
LABELING AI supports various methods to perform Auto-Labeling.
Build a data pipeline for MLOps at DS2.ai with Python. SDK gives you access to all of the processes from uploading and labeling data, to training and deploying the artificial intelligence model.
Learn more →
from ds2 import DS2
ds2 = DS2(apptoken=“s2234k3b4”)
ds2.startAutoLabeling(
"people.zip",
"1000",
has_label_data=True,
ai_type="general",
autolabeling_type="box",
general_ai_type="person",
model_id=None,
custom_ai_stage=0,
workapp="object_detection"
)