Upload RCLane release model artifacts (17-09-2026)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+rclane_b0_e19_fp16.engine filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,36 @@
 ---
 license: mit
+library_name: pytorch
+pipeline_tag: image-segmentation
+tags:
+  - lane-detection
+  - carla
+  - tensorrt
+  - onnx
 ---
+
+# RCLane release model (17-09-2026)
+
+Model artifacts used with the RCLane source release at Git commit
+[`833455bf0046986531913ae7abcbc0dc1fd3e38f`](https://github.com/shinlnh/RCLane/commit/833455bf0046986531913ae7abcbc0dc1fd3e38f).
+
+## Files
+
+| File | Purpose | SHA-256 |
+| --- | --- | --- |
+| `rclane_b0_e19_fp16.engine` | Exact FP16 TensorRT engine used by the deployed lane-detection service | `1dd1ea149559dd5694e450067cd1d34a2299c49973eb91fb06a07bce78037e72` |
+| `rclane_b0_e19_trt85.onnx` | Portable ONNX graph prepared for TensorRT 8.5 | `8847f7da77c5bcdb67c166e119f9817f68aa2d420acbd333128a10826ce363cd` |
+| `rclane_b0_e19.pth` | PyTorch training checkpoint, CARLA B0 epoch 19 | `ef3eb57a7064cb89783144a05344635e1be3a7527dd69a2022b0240782908866` |
+| `manifest.json` | Artifact metadata and checksums | — |
+
+## Interface
+
+- Input: `images`, shape `N x 3 x 320 x 800`
+- Outputs: `seg_map`, `up_arrow`, `down_arrow`, `up_bound`, `down_bound`
+- ONNX opset: 17
+
+TensorRT engines are environment-specific. The included engine is the exact deployed
+artifact and was used on an NVIDIA GeForce RTX 3050 6GB Laptop GPU (compute capability
+8.6). Rebuild the engine from the ONNX file when using a different GPU, TensorRT, CUDA,
+or driver stack.
+

manifest.json

@@ -0,0 +1,42 @@
+{
+  "release": "17-09-2026",
+  "source_repository": "https://github.com/shinlnh/RCLane",
+  "source_commit": "833455bf0046986531913ae7abcbc0dc1fd3e38f",
+  "model": {
+    "architecture": "RCLane B0",
+    "dataset": "CARLA",
+    "epoch": 19,
+    "input": {
+      "name": "images",
+      "shape": ["batch", 3, 320, 800]
+    },
+    "outputs": [
+      "seg_map",
+      "up_arrow",
+      "down_arrow",
+      "up_bound",
+      "down_bound"
+    ],
+    "onnx_opset": 17
+  },
+  "artifacts": [
+    {
+      "path": "rclane_b0_e19_fp16.engine",
+      "bytes": 17927468,
+      "sha256": "1dd1ea149559dd5694e450067cd1d34a2299c49973eb91fb06a07bce78037e72",
+      "role": "deployed TensorRT FP16 engine"
+    },
+    {
+      "path": "rclane_b0_e19_trt85.onnx",
+      "bytes": 25122448,
+      "sha256": "8847f7da77c5bcdb67c166e119f9817f68aa2d420acbd333128a10826ce363cd",
+      "role": "portable TensorRT-compatible ONNX graph"
+    },
+    {
+      "path": "rclane_b0_e19.pth",
+      "bytes": 74931410,
+      "sha256": "ef3eb57a7064cb89783144a05344635e1be3a7527dd69a2022b0240782908866",
+      "role": "PyTorch training checkpoint"
+    }
+  ]
+}

rclane_b0_e19.pth

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+size 74931410

rclane_b0_e19_fp16.engine

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+size 17927468

rclane_b0_e19_trt85.onnx

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+size 25122448