Onnx on DeepLearning.Earthhttps://deeplearning.earth/tags/onnx/Recent content in Onnx on DeepLearning.EarthHugo -- gohugo.ioenMon, 12 Oct 2026 06:30:00 +0700ONNX export in oriented-det — inference without PyTorchhttps://deeplearning.earth/posts/2026-10-08_onnx_export_without_pytorch/Thu, 08 Oct 2026 06:00:00 +0700https://deeplearning.earth/posts/2026-10-08_onnx_export_without_pytorch/Train on the GPU box. Send the infer box a small graph. oriented-det writes a pre-NMS ONNX file. The graph decodes the boxes. A few lines of Python prepare the image and run the rotated NMS. The machine that runs it needs numpy, Pillow, and ONNX Runtime. For a tiled optical scene at 1024 pixels, that is enough to ship this week. The commands are below. ONNX Runtime overlay — Rotated FCOS DOTA 1× Hub on the export demo tile (score ≥ 0.Oriented-Det v0.3.1 — four datasets, ONNX, and RetinaNet OBBhttps://deeplearning.earth/posts/2026-09-21_oriented-det_v0_3_0_four_datasets_and_onnx/Mon, 21 Sep 2026 09:00:00 +0700https://deeplearning.earth/posts/2026-09-21_oriented-det_v0_3_0_four_datasets_and_onnx/Three weeks after v0.2.0, Oriented-Det v0.3.1 is on PyPI and tagged on GitHub. This is the dataset + deploy chapter (0.3.0) plus a same-day patch: Rotated RetinaNet Hub is OBB, odet export is a first-class CLI, and deploy sidecars match Hub class lists. The four ResNet-FPN detector families stay. What landed is four native loaders, an HRSC Hub 3× zoo, a DOTA zoo you should advertise from 1×, and ONNX export.