Replace lane checkpoints with models trained on the sequence-disjoint split; fix card

README.md

@@ -1,11 +1,17 @@
 ---
-pretty_name: AD Winter Driving Dataset
+pretty_name: "AD Winter Driving Dataset: MI-Snow1000 lane detection and MCity-WSPI physics"
 task_categories:
 - image-segmentation
 - object-detection
 tags:
-- autonomous-driving
+- mi-snow1000
 - lane-detection
+- snow
+- winter-driving
+- adverse-weather
+- tusimple
+- autonomous-driving
+- adas
 - vehicle-dynamics
 - weather-telemetry
 license: cc-by-4.0
@@ -13,12 +19,32 @@ size_categories:
 - 100K<n<1M
 ---
 
-# AD Winter Driving Dataset: Multimodal Perception & Physics
+# AD Winter Driving Dataset: MI-Snow1000 Lane Detection and MCity-WSPI Physics
+
+This is the official Hugging Face repository hosting the image binary packages and pre-trained benchmarks for the **AD Winter Driving Dataset**, created by the **AD Assurance Lab** in partnership with **Western Michigan University** and **MCity**. It has two modules:
+
+- **MI-Snow1000** is a lane detection dataset for snow-covered roads: 4,888 dashcam frames (1280x720) from over 1,000 miles of winter driving in Michigan, Colorado and Wyoming, each annotated with the left and right boundaries of the ego lane in TuSimple format. Where snow buries the paint, the boundaries follow the drivable corridor shown by tire tracks and plow lines. It includes a sequence-disjoint train, validation and test split (3,105, 887 and 337 frames), per-frame snow, glare and curvature tags, and trained SCNN, PolyLaneNet, LaneATT and CLRNet checkpoints in PyTorch and ONNX. License CC BY 4.0.
+- **MCity-WSPI** pairs front-camera frames with vehicle dynamics, IMU, GPS and road friction telemetry recorded on snow and ice at Mcity.
+
+For the annotations, splits, synchronized CSV metadata, loaders, and developer documentation, please visit the main GitHub repository:
+👉 [GitHub: AD-Assurance-Lab/dataset--winter-driving--main](https://github.com/AD-Assurance-Lab/dataset--winter-driving--main)
+
+---
+
+## MI-Snow1000 in brief
+
+| | |
+|---|---|
+| Frames | 4,888 annotated dashcam frames, 1280x720 JPEG |
+| Labels | Left and right boundary of the ego lane, TuSimple format (56 rows, 160 to 710; -2 where a boundary is absent) |
+| Benchmark split | `metadata/sequence_disjoint/` on GitHub: 3,105 training, 887 validation, 337 test frames, no drive shared between partitions |
+| Condition tags | `metadata/condition_tags.json` on GitHub: snow severity, glare, curvature, single boundary |
+| Models | `models/` here: SCNN, PolyLaneNet, LaneATT, CLRNet at 320x180, trained on the benchmark training partition (seed 0) |
+| License | CC BY 4.0 |
 
-This is the official Hugging Face repository hosting the image binary packages and pre-trained benchmarks for the **AD Winter Driving Dataset**, created by the **AD Assurance Lab** in partnership with **Western Michigan University** and **MCity**.
+**Frames with no lane boundaries are intentional.** Where the annotators judged that no boundary could be placed with confidence, for example on a fully snow-covered road with no tracks, the frame was kept with no lanes labeled, so that a detector learns to output nothing rather than guess. These are 535 of the 4,888 frames (11%). Treat them as frames whose correct output is no lane, not as missing labels.
 
-For the code repositories, synchronized CSV metadata, loaders, and developer documentation, please visit the main GitHub repository:
-👉 [GitHub: AD-Assurance-Lab/winter-driving-dataset](https://github.com/AD-Assurance-Lab/winter-driving-dataset)
+To ask for an image to be removed, for example one that shows your face or license plate, open an issue on the GitHub repository with the frame name.
 
 ---
 
@@ -28,7 +54,7 @@ This repository is structured to separate tabular metadata, pre-trained models,
 
 ```text
 winter-driving-dataset/
-├── metadata/                 # Tabular metadata and annotations (on GitHub)
+├── metadata/                 # Annotations, splits and synchronized CSVs (on GitHub)
 ├── models/                   # Pre-trained lane detection checkpoints
 │   ├── onnx/                 # ONNX float32/float16 format checkpoints
 │   └── pytorch/              # PyTorch .pt / .pth checkpoints
@@ -38,8 +64,7 @@ winter-driving-dataset/
     │   ├── feb23-straight-10_images.zip
     │   └── ...
     └── reva_perception/
-        ├── labeled_set_images.zip       # Labeled TuSimple-format training images
-        └── non_labeled_set/             # Unlabeled sequence frames
+        └── non_labeled_set/  # Unlabeled MI-Snow1000 drive frames, one zip per session
 └── raw/
     └── mcity_wspi/           # Raw recordings the synchronized CSVs were made from
         ├── csv/<day>/        # Per-topic ROS message exports, 62 runs over four days
@@ -52,7 +77,7 @@ winter-driving-dataset/
 
 ## How to Download and Load the Dataset
 
-Instead of downloading this entire dataset manually, we provide dedicated Python tools inside the [GitHub Repository](https://github.com/AD-Assurance-Lab/winter-driving-dataset).
+Instead of downloading this entire dataset manually, we provide dedicated Python tools inside the [GitHub Repository](https://github.com/AD-Assurance-Lab/dataset--winter-driving--main).
 
 ### 1. Download Specific Run Images
 To download and automatically unzip a specific run package, use the download script:
@@ -99,17 +124,13 @@ hf download AD-Assurance-Lab/winter-driving-dataset --repo-type dataset --includ
 
 ## Citation
 
-If you use this dataset in your research, please cite the following:
+If you use MI-Snow1000, please cite:
 
 ```bibtex
-@article{tye2026misnow1000,
+@misc{tye2026misnow1000,
   title={MI-Snow1000: A Comprehensive Dataset and Benchmark for Lane Detection in Adverse Winter Conditions},
-  author={Tye, Eugene and Clinton, Catherine and Asher, Zachary and Fong, Alvis},
-  journal={SAE International Journal of Connected and Autonomous Vehicles},
-  volume={9},
-  number={2},
-  pages={112--128},
+  author={Tye, Eugene and Fanas Rojas, Johan and Masterson, Alex and Kadav, Parth and Hong, Guan Yue and Asher, Zachary D.},
   year={2026},
-  publisher={SAE International}
+  note={Manuscript under review}
 }
 ```

models/onnx/CLRNet_180_320_fp16.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8d10b5ac70e111284d01ea2ac72573819b27864f025ceacb7eebbaef4d4f9420
-size 44160531
+oid sha256:023882f9b0750c9c39d9aa80fc61a5bd57be2f82a7455b1e03c1a1a20934c28e
+size 44145690

models/onnx/CLRNet_180_320_fp32.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1bdb389ba2793a5add95b82a1ead6f2bc34dfabb7c91802fae5092202dd21dff
-size 87838127
+oid sha256:14591e11dca587bb8eb368b006a1c7b513ebedad5a34e8a52d55938aa9bfc61b
+size 87827325

models/onnx/LaneATT_180_320_fp16.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d42f69db0ecf8dbca12bfb55e9c783fb1ebbb47b245b4f009154ad83ebb68484
-size 56742078
+oid sha256:e1e69d4ba5f6d3f1b5cb11e3c0deef8c1f022031fdf7490b0adb8fe8993ce3f4
+size 52250578

models/onnx/LaneATT_180_320_fp32.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b76d0da6b8e457322b3a1c416043df722962cae174299b3d4ffe3cbbeaef9843
-size 102876586
+oid sha256:1bcd56ea7ef2bfdafdb04ce48c94e8ca62306ca453da89a994c9517b028bb332
+size 96379505

models/onnx/PolyLaneNet_180_320_fp16.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1c58130c539fb4305fbcc9adfcb7b4035bf0c27c646c4ab3fbc7327fa09034ed
-size 42903249
+oid sha256:791c5bab077258ae940d36f392b752bdef7f6e9d9962ae9c0c3ee1ef8e61f9e3
+size 42592776

models/onnx/PolyLaneNet_180_320_fp32.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b18742ccbeef8319a86c432445a4dd03059faef297457e04f935ffbf1b5de95a
-size 85463855
+oid sha256:a2c505bf0b251572026ced255bf7c79e8f4e57c91a1f785ec7a47c4fddf1273b
+size 85153206

models/onnx/SCNN_180_320_fp16.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3ac5056b29f51189b7fe12ee38ae21e0a258b0ab97087d0f46d23d15f4f39e7c
-size 40873084
+oid sha256:13cd1512a69a53ae77472ae3bf9ba704c700b3fa8be7bdb1715dc21ff7b87ddd
+size 40872970

models/onnx/SCNN_180_320_fp32.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f37762fc470769bcc79cc8f1ffbb32c349a61288aa5867e2c5f33016b4ffe228
-size 81530011
+oid sha256:3ede7bb52b792bf8e730c909036e6f80a911c4636e5f7a6b62390d37572917c5
+size 81530019

models/pytorch/CLRNet_180_320_Pytorch.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:15685090344ebbb86e53f171d53f5cdea80a93d63af1cf090b98aa90108012b0
-size 262972273
+oid sha256:ab94c29a7c778d6477eb5c7003ba48ef1d32980aced98656d77fd7e16b270590
+size 262964872

models/pytorch/LaneATT_180_320_Pytorch.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:df66bf18ab5014ecd5eb206f9492478c23c58f4106566490af50d2b0c8530118
-size 260481091
+oid sha256:f7aa46b9381b0ce39f795b0fca3698cb58b70005e7688716edb215be30077f2a
+size 260481933

models/pytorch/PolyLaneNet_180_320_Pytorch.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:49c958bb43638ead4f49ab2a962b3d55a0766d811aa8060fbdabd94e7249895d
-size 255738501
+oid sha256:2ec7443811cac1593b6f0decc0ce6f0ebf0bdacb37d5a90d77949ad9296a164f
+size 255739339

models/pytorch/SCNN_180_320_Pytorch.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:48cd06fcd7ed9e74a26f563a8e01190f2a58018fdecd3d6ff0ba5df7df8b55e5
-size 163000941
+oid sha256:c5aec1274d280ee742f13e578f43757fd85dde169b2f401ac9e80b84e78ffdd9
+size 163001571