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README.md

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+---
+license: mit
+task_categories:
+- image-to-text
+- image-text-to-text
+language:
+- en
+tags:
+- synthetic
+- multimodal
+- scene-graph
+- spatial-reasoning
+- json
+- computer-vision
+- 3d
+pretty_name: TEDRASIM Dataset
+size_categories:
+- 10K<n<100K
+---
+
+
+# TEDRASIM Dataset
+
+## Dataset Summary
+
+This dataset is a multimodal training corpus for fine-tuning vision-language models to generate structured JSON scene-graph descriptions from rendered images.
+
+This dataset contains:
+
+- a synthetic dataset of 10,000 scenes, each rendered from 2 views (20,000 images total),
+- a small real-world dataset of 120 images.
+
+Each example consists of:
+- one image showing a object assembly,
+- a multi-turn chat-style prompt structure,
+- a target JSON string describing the scene in a canonical structured format.
+
+It is intended for research and development on structured visual reasoning, spatial reasoning, scene understanding, and image-to-JSON generation.
+
+--- 
+
+## Data Description
+
+The dataset contains images of a very specific class of toy-like 3D objects.
+
+These objects are:
+
+- composed of geometric primitives such as cubes, sphere, cones,...
+- arranged in simple spatial configurations
+- rendered from multiple viewpoints
+
+---
+
+## Task Definition
+
+The model is expected to generate a JSON scene graph describing the object by relative spatial relationships between a finite set of known fixed primitives
+
+Relationships are defined locally between touching primitives, for example:
+
+- "the blue cube is behind the green cone"
+- "the red cube is left of the orange cylinder"
+
+These relations are encoded explicitly in the JSON structure.
+
+---
+
+## Example Target Representation
+
+A simplified example of a scene description:
+
+```json
+{
+  "primitive_counts": {
+    "red_cube": 2,
+    "yellow_sphere": 1
+  },
+  "primitives": [
+    {
+      "id": "P1",
+      "color": "red",
+      "shape": "cube",
+      "neighbors": {
+        "front": "P2",
+        "back": "Empty Space",
+        "left": "Empty Space",
+        "right": "Empty Space",
+        "up": "Empty Space",
+        "down": "Empty Space"
+      }
+    },
+    {
+      "id": "P2",
+      "color": "yellow",
+      "shape": "sphere",
+      "neighbors": {
+        "front": "Empty Space",
+        "back": "P1",
+        "left": "Empty Space",
+        "right": "Empty Space",
+        "up": "Empty Space",
+        "down": "Empty Space"
+      }
+    }
+  ]
+}
+```
+
+---
+
+## Dataset Structure
+
+### Data Instances
+
+Each record in the JSONL files has the following structure:
+
+```json
+{
+  "image": "train/shard_0000/scene_000000_00.png",
+  "messages": [
+    {"role": "system", "content": "..."},
+    {"role": "user", "content": "..."},
+    {"role": "assistant", "content": "{\"primitive_counts\": ..., \"primitives\": ...}"}
+  ],
+  "meta": {
+    "scene_id": "scene_000000",
+    "scene_hash": "...",
+    "split": "train",
+    "shard": "shard_0000",
+    "view_id": "view_00",
+    "num_primitives_in_scene": 4,
+    "min_primitives": 1,
+    "max_primitives": 6,
+    "seed": 42,
+    "attempt_index": 1,
+    "accepted_index": 0
+  }
+}
+```
+
+### Data Fields
+
+- image: relative path to the rendered image
+- messages: chat-style training structure
+  - system: task instruction
+  - user: input prompt
+  - assistant: target JSON 
+- meta: auxiliary metadata for traceability
+
+---
+
+## Splits
+
+The synthetic dataset is divided into:
+
+- train.jsonl
+- val.jsonl
+- test.jsonl
+
+The real dataset contains validation data only:
+
+- val.jsonl
+
+
+---
+
+## Repository Layout
+
+- synthetic/: synthetic dataset described here
+- real/: real-world dataset component 
+- train.jsonl / val.jsonl / test.jsonl: split manifests
+
+---
+
+## License
+
+MIT