tao-train-fast-foundation-stereo

You train stereo depth estimation models by running the train action with your specifications to build FastFoundationStereo models. This workflow handles training and evaluation for real-time stereo matching and disparity map prediction.

You export trained stereo matching models by converting them into ONNX format. This action prepares the FastFoundationStereo model for deployment, allowing you to run real-time inference on stereo image pairs.

Yes, you need Docker and nvidia-container-toolkit to execute the FastFoundationStereo workflow. These dependencies provide the required containerized environment to run inference and process stereo image pairs for disparity prediction.

Yes, you can evaluate stereo depth estimation models during the training workflow. The evaluation step checks the FastFoundationStereo model's accuracy before you export it to ONNX format for deployment.

Real-time stereo depth estimation predicts disparity maps from stereo image pairs. It applies to computer vision workflows involving stereo matching, enabling depth perception for systems requiring immediate spatial analysis.

You run inference on stereo image pairs by deploying the exported ONNX model. This process predicts disparity maps for real-time stereo depth estimation tasks in computer vision workflows.