Model card: FlowPilot-DST FastViT-T12 and DUNE ViT-B/14

README.md

@@ -1,3 +1,74 @@
 ---
 license: apache-2.0
+library_name: visnavkit
+pipeline_tag: robotics
+tags:
+  - visual-navigation
+  - flow-matching
+  - onnx
+  - robotics
 ---
+
+# Visual Navigation Model Checkpoints
+
+Pretrained navigation policies trained with [VisNavKit](https://github.com/VAIL-UCLA/visnavkit).
+Each folder holds one model: the Lightning checkpoint, its ONNX export, the export metadata
+(shapes, anchor times, SHA-256 of both files, PyTorch/ONNX parity) and a sample input batch.
+
+| Folder | VisNavKit config | Model | Params | Val top-1 ADE@1/2/4 s (m) | Val top-1 FDE (m) |
+| --- | --- | --- | --- | --- | --- |
+| [`flowpilot-dst-small`](flowpilot-dst-small) | `experiment=flowpilot_dst_clips1k` | FastViT-T12 on frame pairs -> anchored flow DiT (256-d) | 21.4M | 0.093 / 0.193 / 0.422 | 0.897 |
+| [`flowpilot-dst-dune`](flowpilot-dst-dune) | `experiment=flowpilot_dune_dst_clips1k` | frozen DUNE ViT-B/14 -> anchored flow DiT (1024-d) | 208.2M | 0.080 / 0.168 / 0.364 | 0.769 |
+
+Both are FlowPilot-DST policies trained on clips1k (20 Hz, 4 s horizon, point goal with 50%
+dropout, frozen route VAE, 64 k-means anchors, 4 flow steps). Metrics are on the VisNavKit
+clips1k validation split, decoded from zero noise. ONNX: fp32, opset 17, batch 1, top-6 plans;
+PyTorch/ONNX parity is within 1e-5.
+
+## Files
+
+```
+<folder>/flowpilot_dst_<encoder>.ckpt           # Lightning checkpoint (weights + optimizer state)
+<folder>/flowpilot_dst_<encoder>.onnx           # deployment graph
+<folder>/flowpilot_dst_<encoder>.metadata.json  # shapes, anchor times, hashes, parity
+<folder>/flowpilot_dst_<encoder>.inputs.npz     # traced sample inputs for a smoke run
+```
+
+## Usage
+
+ONNX, no VisNavKit needed:
+
+```python
+import numpy as np, onnxruntime as ort
+from huggingface_hub import hf_hub_download
+
+repo, stem = "UCLA-VAIL/Visual-Navigation-Model-Checkpoints", "flowpilot-dst-small/flowpilot_dst_fastvit_t12"
+session = ort.InferenceSession(hf_hub_download(repo, f"{stem}.onnx"), providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
+feeds = dict(np.load(hf_hub_download(repo, f"{stem}.inputs.npz")))  # replace with live data
+modes, probs, speed = session.run(["modes", "probs", "speed"], feeds)
+x, y, yaw, v, w = modes[0, 0].T  # best plan: 80 steps at 0.05 s, ego frame (x forward, y left)
+```
+
+Inputs are the last 20 frames (1, 20, 3, 216, 384) RGB in [0, 1], route patches, goal, ego
+speed/yaw rate and action bounds. The full contract, including frame preparation, is in
+[FlowPilot-DST ONNX IO](https://github.com/VAIL-UCLA/visnavkit/blob/dev/docs/flowpilot_dst_onnx.md).
+
+Re-export from the checkpoint with VisNavKit:
+
+```bash
+uv run visnavkit-export-dst checkpoint=flowpilot_dst_fastvit_t12.ckpt output=flowpilot_dst_fastvit_t12.onnx
+```
+
+## Citation
+
+```bibtex
+@Misc{visnavkit2026,
+  author       = {Honglin He and Bolei Zhou},
+  title        = {{VisNavKit}: a composable toolkit for visual navigation policies},
+  howpublished = {\url{https://github.com/VAIL-UCLA/visnavkit}},
+  year         = {2026},
+}
+```
+
+FlowPilot: [arXiv:2606.12603](https://arxiv.org/abs/2606.12603). The DUNE variant uses the
+[DUNE](https://github.com/naver/dune) ViT-B/14 encoder.