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README.md

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+---
+license: other
+license_name: dinov3-license
+license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license/
+tags:
+  - depth-estimation
+  - semantic-segmentation
+  - onnx
+  - robotics
+  - ros2
+  - jetson
+  - dinov3
+library_name: onnx
+pipeline_tag: depth-estimation
+---
+
+# M2H-MX ONNX weights — monocular depth + semantics
+
+Exported ONNX weights for the two **M2H-MX** networks used by
+[`vio_stack_jetson`](https://github.com/BavanthaU/vio_stack_jetson), a monocular
+VIO stack running on a Jetson Orin with a ZED X One GS camera.
+
+Both networks take a single RGB frame and produce **metric depth** and
+**per-pixel semantic labels** in one pass. Both publish the same two ROS 2
+topics, so they are swappable at runtime:
+
+```
+/m2h/depth/image             32FC1 metric depth
+/m2h/semantic/labels_argmax  mono8 class IDs
+```
+
+**Built with DINOv3.**
+
+## Files
+
+The layout mirrors the ROS packages that consume these files, so a manifest line
+maps to a path with no translation:
+
+| File | Size | Notes |
+|---|---|---|
+| `m2h_mx_base_onnx_ros/models/scannet/m2h_mx_b_scannet_240x320.onnx` | 421 MB | base network, default |
+| `m2h_mx_base_onnx_ros/models/scannet/m2h_mx_b_scannet_480x640.onnx` | 422 MB | base network, full res |
+| `m2h_mx_base_onnx_ros/models/nyudv2/m2h_mx_b_nyudv2_240x320.onnx` | 494 MB | NYUDv2-trained variant |
+| `m2h_mx_base_onnx_ros/models/nyudv2/m2h_mx_b_nyudv2_480x640.onnx` | 496 MB | NYUDv2-trained variant |
+| `m2h_mx_large_onnx/onnx_models/scannet_depth_sem_192x256_trt_clean.onnx` | 1.26 GB | large network, default |
+| `m2h_mx_large_onnx/onnx_models/scannet_depth_sem_320x416.onnx` | 1.26 GB | large network, higher res |
+
+`MANIFEST.sha256` in each package directory lists the expected SHA256 of every
+file. Verify after downloading — a network running on the wrong weights produces
+plausible-looking depth rather than an error, which is much harder to notice than
+a refusal to start.
+
+## Usage
+
+```bash
+git clone https://github.com/BavanthaU/vio_stack_jetson
+cd vio_stack_jetson
+./tools/fetch_weights.sh          # pulls these files and verifies every sum
+```
+
+Or directly:
+
+```python
+from huggingface_hub import snapshot_download
+snapshot_download(repo_id="Bavantha11/vio-stack-jetson-weights", repo_type="model")
+```
+
+## Measured on the rig
+
+Jetson Orin, JetPack r36.5, ONNX Runtime with the TensorRT execution provider:
+
+| Network | Resolution | Latency | Rate |
+|---|---|---|---|
+| base | 240x320 | ~130 ms/frame | ~6.9 Hz |
+| large | 192x256 | ~155 ms/frame | — |
+
+The first run at any resolution builds a TensorRT engine — minutes, once, cached
+per machine. The engines are **not** portable across machines or driver versions
+and are deliberately not published here.
+
+Depth is not fed back into the VIO filter by default; it runs alongside it.
+
+## Architecture
+
+- **Backbone:** DINOv3 ViT-B/16 (`dinov3-vitb16-pretrain-lvd1689m`)
+- **Heads:** two — semantic segmentation (40 classes) and metric depth
+- **Opset:** 17, static shapes
+- **Training data:** ScanNet, NYUDv2
+
+## Licensing — read before use
+
+These weights combine inputs with different terms. The most restrictive one
+governs the result.
+
+**DINOv3 backbone** — [DINOv3 License](https://ai.meta.com/resources/models-and-libraries/dinov3-license/),
+included here as `DINOv3_LICENSE.md`. It permits commercial use, modification and
+redistribution of derivative works, and asks in return that redistributions carry
+a copy of the licence and display "Built with DINOv3". Both are satisfied above.
+
+**ScanNet** — [Terms of Use](http://kaldir.vc.in.tum.de/scannet/ScanNet_TOS.pdf).
+**Non-commercial research and educational use only**, and those terms extend to
+derivative works.
+
+**NYUDv2** — research use.
+
+**Therefore these weights are for non-commercial research and educational use
+only.** ScanNet is the binding constraint; DINOv3 permitting commercial use does
+not lift it. Anyone needing a commercial deployment must retrain on commercially
+licensed data — the ROS packages and the export pipeline are MIT and unaffected.
+
+The MIT licence on the `m2h_mx_*` ROS packages covers the **wrapper code only**,
+not these weights.
+
+## Citation
+
+If you use these in published work, please acknowledge DINOv3 and cite the
+ScanNet and NYUDv2 datasets as their terms require.