Why train a model in pose estimation? Many functions require pose estimation, such as... 🤖Defining the orientation of an object on a conveyer belt for pick and place operations with a robotic arm, 🤖 Replicating an object's pose for AR and VR application overlays, 🤖 Localizing and mapping an environment for path navigation, 🤖 ...and more!
What makes this dataset useful for training a model?
💠Duality AI's digital twins are not generated by AI, but are instead crafted by 3D artists. This allows the training from this data to transfer into real-world applicability. Read more about our techniques for bridging the Sim2Real gap here: https://www.duality.ai/blog/ml-synthetic-data-model 💠The simulation software, called FalconEditor, can easily create thousands of images with varying lighting, posing, occlusions, backgrounds, camera positions, and more using a versatile python API 💠FalconEditor is able to create 100% accurate labels at the SAME TIME as it creates the images. This makes collecting data for tasks such pose-estimation easier, faster, and more reliable.
Interested in a specific dataset? Let us know! We're always looking for new ideas on what datasets to create next 👀
🚀 Duality is super excited to announce that our Kaggle competition is LIVE! Synthetic-to-Real Object Detection Challenge is LIVE! 🚦 Want to master AI training, learn industry-proven synthetic data workflows, and compete for public recognition and cash prizes?
Compete to build the top-performing model capable of detecting real-world objects—trained entirely on synthetic data. Master these industry-proven methods for faster, more targeted, and diverse dataset creation, and set yourself apart, unlocking today's most exciting AI opportunities.
Ready to test your skills?
🏆 The Challenge
Train an object detection model using synthetic images created with Falcon—Duality AI's cutting-edge digital twin simulation software—then evaluate your model on real-world imagery.
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