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The Revolution in AI Training

Unlocking Corner Case Data with JoinAI

all images & labels generated by Simulaix

Encountering Challenges in Gathering Corner Case Data?Gathering Corner Case Data?

Rare Scenarios Are Too Scarce

Those uncommon or once-in-a-lifetime data scenarios are as valuable as gold and hard to come by.

Concerning Annotation Quality

The quality of manually annotated labels is always inconsistent, and in corner cases, it becomes a disaster.

Insufficient Data Diversity

The data that can be collected fails to cover the diversity of the real world.

The Real World Is Full of Black Swan

There is always unpredictability in real life that cannot be anticipated.

Challenges in Data Scaling

Collecting massive amounts of data sufficient to trigger scaling laws is daunting.

Now

Imagine

Acquiring Data

as Simple as

Chatting

as Simple as

JoinAI Simulaix animation transition frame showing a snapping hand gesture, followed by the generation process.

Finn Ferral

citystreets,sunny day,daylight,trees,cars,buildings,traffic cones

(Bs : 1200x760, 4, 10, 4)

Simulaix-frontview

Finn Ferral

citystreets,sunny day,daylight,trees,cars,buildings,traffic cones

(preparing to start)

(Bs : 1200x760, 4, 10, 4)

Chatting

One Prompt,
Infinite Possibilities

Concerned Whether Generated Data Can Truly Enhance Your AI?

lets see the results

In the actual commercial deployment process, we have successfully helped customers improve algorithm performance through generated data for conventional object classification, corner cases, and various sensor perspectives including fisheye cameras. The data listed in these three tables represent real data that we have been authorized by the customer to disclose publicly during the PoC (Proof of Concept) stage.

Usability Testing of Synthetic data for L4 Autonomous Driving in Urban Scenarios

Training Data Set	Algorithm	Validation Set	Object Detection	Precision	MIOU
10K Road-collected Data	Customer's Proprietary Algorithm	Customer-Collected Validation Set	20 categories including car, pedestrian, etc.	0.87	45.7
10K Generated Data	Customer's Proprietary Algorithm	Customer-Collected Validation Set	20 categories including car, pedestrian, etc.	0.84	41.8
10K Road-collected data + 10K Generated Data	Customer's Proprietary Algorithm	Customer-Collected Validation Set	20 categories including car, pedestrian, etc.	0.92	48.9

Types of Customers：Level 4 Autonomous Driving for Commercial Vehicles on Urban Roads

Sensor：camera

Study Result：Mixing generated data with road-collected data effectively improves algorithm performance in this scenario.

Usability Testing of Synthetic data for L4 Autonomous Driving in Long-tail Scenarios: Traffic Cones

Training Data Set	Algorithm	Validation Set	Object Detection	Precision	MIOU
0.5K Road-collected Data	Customer's Proprietary Algorithm	Customer-Collected Validation Set	Traffic Cone	0.31	36.7
5K Generated Data	Customer's Proprietary Algorithm	Customer-Collected Validation Set	Traffic Cone	0.94	78.8
0.5K Road-collected Data + 5K Generated Data	Customer's Proprietary Algorithm	Customer-Collected Validation Set	Traffic Cone	0.95	81.4

Types of Customers：Level 4 Autonomous Driving for Commercial Vehicles on Urban Roads

Sensor：camera

Study Result：In data-scarce long-tail scenarios, generated data can significantly enhance algorithmic capabilities.

Usability Testing of Synthetic data for L4 Autonomous Driving in Urban Scenarios

Training Data Set	Algorithm	Validation Set	Object Detection	Precision	Recall
5K Road-collected Fisheye Data	yolov8x	Customer-Collected Validation Set	pedestrian	0.84	0.82
5K Generated Fisheye Data	yolov8x	Customer-Collected Validation Set	pedestrian	0.86	0.84
5K Road-collected Fisheye Data + 5K Generated Fisheye Data	yolov8x	Customer-Collected Validation Set	pedestrian	0.91	0.89

Types of Customers：L2.5 Commercial Autonomous Driving

Sensor：fisheye camera

Study Result：Generated data can adapt to different types of sensors and achieve good results on special sensors as well.

OpenAI CEO Sam Altman quipped that

he is pretty confident that soon all data will be synthetic data

Google Research, Brain Team

The Google Brain team demonstrated in their paper "Synthetic Data from Diffusion Models Improves ImageNet Classification" that synthetic data generated by diffusion models has reached a usable level：Deep generative models, fine-tuned from text prompts, set new benchmarks in ImageNet classification accuracy.

Synthesis.ai、Google、Columbia University

Google and its partners demonstrated the effective value of synthetic data in solving challenges in their paper "ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation"：To train and test ClearGrasp, we developed a dataset with over 50,000 synthetic RGB-D images and a real test benchmark of 286 transparent objects, proving that ClearGrasp significantly surpasses existing methods in depth estimation and generalization capabilities.

MIT, MIT-IBM Watson AI Lab, Goethe University, Boston University

MIT demonstrated in their paper "How Transferable are Video Representations Based on Synthetic Data?" the potential for synthetic data to surpass real data in training effectiveness：This study introduces SynAPT, a new benchmark for action recognition, exploring the application of synthetic video data in addressing challenges of large-scale video datasets, finding that synthetic data can surpass real data performance under specific conditions.

Worry about Model Autophagy Disorder (MAD)？

see our latest blog we did

Safeguarding Privacy:
Our Commitment in Every Generated Dataset

We adhere to strict privacy standards ensuring that every piece of generated data is free from privacy infringements and legal concerns.

Transparent Data Processing Methods

We maintain complete transparency in our data processing methods, providing clients with the assurance that their AI training is built on secure and ethical foundations.

No Real Identity Data Usage

Our AI-driven data generation process guarantees that no real individuals' identities or private information are used, thereby eliminating concerns of privacy breaches.

Compliance with International Regulations and Audits

We strictly adhere to international privacy laws, including GDPR, BIPA, and other US and European regulations, and conduct regular audits and compliance checks to ensure the legal and ethical integrity of our data generation process

Advanced Data Security Measures

We implement advanced security measures to protect the data we generate and handle, ensuring the highest level of protection against unauthorized access or breaches.

Adherence to Geographical Privacy and Cultural Compliance

Our generated topographical data strictly adhere to physical logic and local cultural norms without replicating actual geographic locations or landmarks, ensuring freedom from political risks and respect for global cultural diversity.

Customizable Privacy Compliance and Geographical Consideration

Understanding the unique privacy legislations of different regions, we offer customizable solutions to align with specific local or national privacy regulations. Our commitment extends to adapting our data generation to respect geographical privacy and cultural norms, ensuring compliance no matter where you operate.

Revolutionize Your AI Training with

Label-to-Training Data

Simultaneous Generation of Original Data and Its Annotations

By restructuring the generation model's process, we achieve simultaneous extraction and creation of precise pixel-level object annotations and regional information while generating images, enhancing data processing efficiency and accuracy.

Three-Way Linking of Label, Dataset, and AI Model

The platform's three-way linking and version management capabilities ensure complete coordination and version control among labels, datasets, and models, enhancing project management efficiency.

Unlimited Customization Capabilities

Thanks to our powerful proprietary models and open training architecture, users can customize everything from label types and raw data styles to the generation simulator itself, meeting precise, individualized needs.

Multi-Sensor Support

Supports a wide range of visual sensor data simulation, including but not limited to 2D cameras, to meet diverse application needs.

Large-Scale Data Generation in Time and Space

Utilizing our proprietary world model, Terra-1, we can generate images featuring multiple objects in large scenes as well as continuous frame images and video data over time, significantly enriching training materials.

Realistic and Controllable Data Generation

Utilizing our proprietary world model, Terra-1, and Simulaix technologies, we support the customization of multidimensional image data that adheres to physical laws and achieves a lifelike realism. This ensures high-quality output and broad applicability based on user needs.

Join the AI Evolution with

Simulaix by JoinAI

Pioneering Data for Smarter AI Futures

The Revolution in AI Training

Encountering Challenges in Gathering Corner Case Data?

Rare Scenarios Are Too Scarce

Concerning Annotation Quality

Insufficient Data Diversity

The Real World Is Full of Black Swan

Challenges in Data Scaling

NowImagine Acquiring Dataas Simple asChatting

One Prompt,Infinite Possibilities

Concerned Whether Generated Data Can Truly Enhance Your AI?

lets see the results

Usability Testing of Synthetic data for L4 Autonomous Driving in Urban Scenarios

Usability Testing of Synthetic data for L4 Autonomous Driving in Long-tail Scenarios: Traffic Cones

Usability Testing of Synthetic data for L4 Autonomous Driving in Urban Scenarios

Worry about Model Autophagy Disorder (MAD)？

Safeguarding Privacy:Our Commitment in Every Generated Dataset

Transparent Data Processing Methods

No Real Identity Data Usage

Compliance with International Regulations and Audits

Advanced Data Security Measures

Adherence to Geographical Privacy and Cultural Compliance

Customizable Privacy Compliance and Geographical Consideration

Revolutionize Your AI Training with Label-to-Training Data

Simultaneous Generation of Original Data and Its Annotations

Three-Way Linking of Label, Dataset, and AI Model

Unlimited Customization Capabilities

Multi-Sensor Support

Large-Scale Data Generation in Time and Space

Realistic and Controllable Data Generation

One Prompt,
Infinite Possibilities

Safeguarding Privacy:
Our Commitment in Every Generated Dataset

Revolutionize Your AI Training with

Label-to-Training Data