H1 Procedural Humans for Computer Vision

H2: What Is Synthetic Data?

Synthetic data is any information manufactured artificially which does not represent events or objects in the real world. Algorithms create synthetic data used in model datasets for testing or training purposes. The synthetic data can mimic operational or production data and help train machine learning (ML) models or test out mathematical models.
Synthetic data offers several important benefits: it minimizes the constraints associated with the use of regulated or sensitive data, it can be used to customize data to match conditions that real data does not allow, and it can be used to generate large training datasets without requiring manual labeling of data.

H2: Advantages of Synthetic Data

Data scientists shouldn’t mind if the data they use is authentic or synthetic, as long as it represents accurate patterns, is balanced, unbiased, and high quality. Synthetic data allows for enrichment and optimization, which allows data scientists to unlock several advantages:

• Data quality – in addition to being complicated and expensive to collect, real-world data is often full of errors, containing inaccuracies or representing a bias that may affect the quality of a neural network. Synthetic data ensures higher data quality, balance, and variety. Artificially-generated data can automatically fill in missing values and apply labels, enabling more accurate predictions.
• Scalability – machine learning requires massive amounts of data. It is often difficult to obtain relevant data on the necessary scale for training and testing a predictive model. Synthetic data helps fill in the gaps, supplementing real-world data to achieve a larger scale of inputs.
• Ease of use – synthetic data is often simpler to generate and use. When collecting real-world data, it is often necessary to ensure privacy, filter out errors, or convert data from disparate formats. Synthetic data eliminates inaccuracies and duplicates and ensures all data has a uniform format and labeling.

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Figure 2: Sampled images from 300-W Train that are used in the validation set

Figure 2: Sampled images from 300-W Train that are used in the validation set

Figure 2: Sampled images from 300-W Train that are used in the validation set

Figure 2: Sampled images from 300-W Train that are used in the validation set

Figure 2: Sampled images from 300-W Train that are used in the validation set

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# get a random In-Cabin sequence
sequence = random.choice(catalog.sequences.get
(domain=attributes.HICDomain.IN_CABIN)
# set the camera
sequence.camera.extrinsic_params = 
\ic_sequence.presets.cameras['media_dashboard_camera_cabin_view']
# set clutter level and area
sequence.clutter_areas['front_passenger_seat']=assets.ClutterLevel.MEDIUM
# set the background
sequence.background = catalog.backgrounds.get(limit=1)
# create the data request
request = assets.DataRequest(data=[sequence])
# generate the data
datapoint_api_generate(request) Shared in