No, not by code submission alone: the README explicitly says that model-zoo expansion is currently frozen and new integrations require a named maintenance sponsor.

• The AI Models section states that the model zoo is “currently frozen for expansion” while maintainer capacity is focused on the geometry core.
• Existing models such as LoFTR, LightGlue, DISK, DeDoDe, and SAM remain available and maintained, but that does not imply that new model categories are open for addition.
• The README specifically requires every new integration, including VLM/VLA models, to have a named maintainer sponsor who accepts ongoing ownership; “a contributor implementation alone cannot reopen the surface.” Your implementation and tests are therefore not sufficient by themselves.

No, not as a direct migration: your workload does not use Kornia’s strongest advantages—batched tensors, GPU execution, and autograd—and may instead add PyTorch tensor-conversion overhead.

• The project insights explicitly state that Kornia’s main advantages appear in batched Tensor, GPU, and PyTorch workflows; for single images, simple fixed-parameter operations, or CPU uint8 processing, OpenCV, Pillow, scikit-image, or torchvision may be simpler or faster.
• The README positions Kornia as a differentiable vision library built on PyTorch, indicating that its design target is not the shortest path for conventional single-image CPU APIs.
• Reconsidering it would make more sense if the service later needs augmentation inside a training graph, batched GPU inference, or geometric vision operations.

Yes, because Kornia is built around differentiable PyTorch tensor operators rather than CPU-only preprocessing outside the training graph.

• The README describes it as a “differentiable computer vision library” and explicitly highlights batch transformations, auto-differentiation, and GPU acceleration.
• Its stated geometry core includes warping, homographies, cameras, epipolar geometry, rotations, Lie groups, and geometry-consistent augmentation, which align well with camera-pose training.
• However, the whole library should not be assumed differentiable: the project insights state that RANSAC and some learned feature-extraction paths do not guarantee gradients, so the exact operators must be checked individually.

Yes, because Kornia explicitly provides augmentation pipelines intended to transform images and geometric annotations together, matching the alignment problem in detection and keypoint training.

• The project insights describe a unified augmentation pipeline for synchronized images, masks, bounding boxes, and keypoints, with inverse 2D transforms.
• The README Quick Start imports AugmentationSequential, RandomAffine, and RandomBrightness, then applies augmentation_pipeline(img) to a batch, showing that this is a public usage path.
• Interpolation, cropping, keypoint visibility, and coordinate conventions still affect correctness. The project insights specifically call for validating mask interpolation, box cropping, keypoint visibility, and inverse-transform behavior.

It depends: the README demonstrates model chaining and export, but production suitability depends on the exact operators, input shapes, and target runtime.

• The README’s ONNXSequential example chains a Kornia operator, an RT-DETR model from Hugging Face, and “YOUR_OWN_MODEL.onnx”, then exports chained_model.onnx. This directly supports the intended composition workflow.
• The README states that ONNX features require extra packages, so installing only the base package does not guarantee a complete export environment.
• The project insights note that export support varies by operator and configuration; complex control flow, dynamic shapes, and particular models still require individual validation. ONNXSequential is therefore not a guarantee of universal operator compatibility.

It depends: Kornia matches the batched GPU geometry workflow, but numerical risks in half-precision robust estimation are not eliminated by the README.

• The project insights list batched minimal solvers, RANSAC, PnP, fundamental/essential-matrix estimation, and CUDA/half-precision support, fitting parallel processing of many geometric hypotheses on tensors.
• The README emphasizes conformance tests, explicit conventions, and honest benchmarks, showing that geometric correctness and device performance are both project concerns.
• RANSAC generally does not guarantee gradients, and the project insights warn that half precision can reduce stability for degenerate cases, small angles, or large-scale coordinates. Half precision therefore should not be assumed safe for every solver stage.