Fine-Tuning LLMs: RLHF, LoRA, and Instruction Tuning
We continue our series on generative AI. We have...
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
We continue our series on generative AI. We have...
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
We continue our series on generative AI. We have...
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
We continue our series on generative AI. We have...
Together, we’re building the future of computer vision & machine learning
Facial identification and verification for consumer and security applications.
Activity recognition and threat detection across camera views.
Spatial computing, gesture recognition, and gaze estimation for headsets.
Millions of identities and clothing options to train best-in-class models.
Simulate driver and occupant behavior captured with multi-modal cameras.
Simulate edge cases and rare events to ensure the robust performance of autonomous vehicles.
We continue our series on generative AI. We have...
Together, we’re building the future of computer vision & machine learning
Synthetic Data for Deep Learning discusses fundamental computer vision problems, both low-level and high-level, synthetic environments and datasets for outdoor and urban scenes (i.e. autonomous driving), indoor scenes (i.e. indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision.
– Springer, Publisher