Add init dataset

.gitattributes

@@ -61,1063 +61,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,206 +1,3 @@
 ---
-pretty_name: ISU Challenge Dataset
 license: mit
-task_categories:
-  - image-segmentation
-  - image-classification
-task_ids:
-  - semantic-segmentation
-  - multi-label-classification
-language:
-  - en
-size_categories:
-  - 1K<n<10K
-annotations_creators:
-  - machine-generated
-source_datasets:
-  - original
-tags:
-  - synthetic
-  - computer-vision
-  - automotive
-  - blender
 ---
-
-# Dataset Card for ISU Challenge Dataset
-
-## Dataset Description
-
-- Homepage: http://isu-challenge.github.io/
-- Repository: https://huggingface.co/datasets/ISU-Test/isu-challenge-dataset
-- Point of Contact: [Lev Sorokin](lev.sorokin@tum.de).
-
-### Dataset Summary
-
-ISU Challenge Dataset is a multi-modal in-cabin automotive dataset with a controlled synthetic core and paired real-reference scenes.
-
-The synthetic core contains 1,000 synchronized Blender-rendered samples with:
-
-- RGB render
-- depth (EXR and PNG)
-- instance segmentation
-- canny edge map
-- structured scenario labels
-
-An additional 30 paired real-reference scenes occupy `sample_00000` through `sample_00029`. Each paired scene contains `real/<stem>_real.jpg`, its matching `images/<stem>_sim.png`, structured labels, and depth/Canny/instance-segmentation auxiliaries rendered from those labels. The original synthetic records `sample_00000` through `sample_00029` are relocated to `sample_01000` through `sample_01029`.
-
-Another 30 paired scenes are withheld for the ISU Challenge evaluation and are not part of this release. Revisions before 2026-10-26 contained them as `sample_00030` through `sample_00059`; these names now hold synthetic records again, their names in the initial release (they were `sample_01030` through `sample_01059` in between).
-
-The simulated image and the depth, Canny, and instance-segmentation channels of the paired scenes were re-rendered from their labels on 2026-10-23 (scenes with a driver phone again on 2026-10-26, see below) with the scene and pipeline of the synthetic core (`scene_v4.blend`), so all channels of a scene align. Earlier revisions combined simulated images from an older scene with channels from `scene_v4.blend`. Where the driver holds a phone, the paired render follows the pose of the real photo (in the lap or at the ear); the synthetic core always shows it at the ear. The label `driver_phone: YES` covers both poses.
-
-Each sample is linked through a `scene_manifest.json` entry and shares its canonical name across modalities.
-
-### Supported Tasks
-
-This dataset can support:
-
-- Semantic segmentation using instance segmentation masks.
-- Multi-label classification for cabin state/safety/context variables from labels JSON.
-- Depth estimation benchmarking with paired RGB and depth.
-- Edge-focused pretraining/evaluation using canny maps.
-
-
-### Languages
-
-No natural-language corpus is included. Label keys and categorical values are English identifiers.
-
-## Dataset Structure
-
-Top-level layout:
-
-- canny/
-- depth/exr/
-- depth/png/
-- images/
-- instance_seg/
-- labels/
-- real/
-- scene_manifest.json
-
-All logical records use the `sample_XXXXX` format. For paired records `sample_00000` through `sample_00029`, the real photo is `real/<stem>_real.jpg` and its matching synthetic image is `images/<stem>_sim.png`. The remaining synthetic-only records are `sample_00030` through `sample_01029`.
-
-Manifest records retain paths and structured parameters for traceability. Per-sample annotations are provided under `labels/`.
-
-### Data Instances
-
-A typical manifest record:
-
-```json
-{
-	"sample_index": 0,
-	"sample_name": "sample_00000",
-	"json_path": "labels/sample_00000.json",
-	"image_path": "images/sample_00000_sim.png",
-	"real_image_path": "real/sample_00000_real.jpg",
-	"depth_path": "depth/png/sample_00000_depth.png",
-	"instance_seg_path": "instance_seg/sample_00000_instance_seg.png",
-	"canny_path": "canny/sample_00000_canny.png",
-	"params": {
-		"env": "HIGHWAY",
-		"driver_gender": "FEMALE",
-		"driver_safety_belt": "NO",
-		"passenger_codriver": "NO",
-		"suitcase": "YES",
-		"driver_height_m": 1.96,
-		"driver_weight_kg": 83.4,
-		"light_front": 0.31,
-		"env_strength": 0.92
-	}
-}
-```
-
-For a paired reference scene, the record additionally contains:
-
-```json
-{
-	"sample_name": "sample_00000",
-	"real_image_path": "real/sample_00000_real.jpg"
-}
-```
-
-### Data Fields
-
-- sample_index (int): Unique sample id.
-- sample_name (string): Canonical ID, either `sample_XXXXX` or `<timestamp>_real`.
-- json_path (string): Path to scene label.
-- image_path (string): Path to RGB rendered image.
-- real_image_path (string, optional): Path to the paired real-reference JPG.
-- depth_path (string): Path to normalized PNG depth map; the matching EXR is stored under `depth/exr/`.
-- instance_seg_path (string): Path to instance segmentation mask.
-- canny_path (string): Path to canny edge image.
-- params (object): Structured labels/conditions for the scene, including environment, occupants, safety-belt states, props, and continuous lighting/photometric parameters.
-
-### Data Splits
-
-This release contains a single split (train-like full set).
-
-| Split | Samples |
-|---|---:|
-| synthetic core | 1000 |
-| paired real-reference scenes | 30 |
-| all logical scene records | 1030 |
-
-## Dataset Creation
-
-### Curation Rationale
-
-The dataset was created to support repeatable, controlled in-cabin perception experiments where scene factors can be explicitly configured and audited.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-The synthetic core and paired auxiliary modalities are generated from structured scenario configurations. Paired real-reference JPGs are separately collected reference images that use the matching scenario labels. The Blender renders use:
-
-- Renderer: Cycles
-- Resolution: 960x540
-- Seed: 42
-
-### Annotations
-
-#### Annotation process
-
-Synthetic annotations are generated by the simulation pipeline. Labels for paired real-reference scenes are stored as matching JSON files and mirrored in their manifest parameters.
-
-### Personal and Sensitive Information
-
-The synthetic core contains no real-person photos. The paired real-reference subset contains real images; confirm appropriate consent, privacy review, and redistribution permissions before publishing or sharing that subset.
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-- Faster iteration for in-cabin vision models in safety-related scenarios.
-- Reproducible controlled testing of feature interactions.
-
-### Discussion of Biases
-
-Bias may arise from design choices in:
-
-- Scenario parameter ranges.
-- Asset library and occupancy patterns.
-- Lighting/environment priors.
-
-## Additional Information
-
-### Dataset Curators
-
-Lev Sorokin, Rifaath Ameen, Stefano Carlo Lambertenghi, Chen Yang.
-
-### Licensing Information
-
-MIT License.
-
-### Citation Information
-
-If you use this dataset, please cite:
-
-```bibtex
-@inproceedings{sorokin2026isutest,
-  author    = {Lev Sorokin and Chen Yang and Ken E. Friedl and Andrea Stocco},
-  title     = {Search-based Testing of Vision Language Models for In-Car Scene Understanding},
-  booktitle = {Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE 2026), Industry Track},
-  year      = {2026},
-  doi       = {10.1145/3832783.3834506}
-}
-```