| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: system |
| dtype: string |
| - name: instruction |
| dtype: string |
| - name: code |
| dtype: string |
| - name: stem |
| dtype: string |
| - name: source_url |
| dtype: string |
| - name: attempts |
| dtype: int32 |
| - name: model |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 118487802 |
| num_examples: 128 |
| download_size: 113967824 |
| dataset_size: 118487802 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| language: |
| - en |
| task_categories: |
| - image-to-text |
| - image-text-to-text |
| tags: |
| - threejs |
| - vision |
| - code-generation |
| - supervised-fine-tuning |
| - round-50 |
| size_categories: |
| - n<1K |
| --- |
| |
| # Round 50 — image-to-Three.js training data |
|
|
| 128 reference images paired with the exact JavaScript modules from the specified `generated_3` submission. All samples are in the `train` split, in canonical `prompts.txt` order, with image bytes embedded in Parquet. |
|
|
| ## Sources |
|
|
| - [Round 50 prompts](https://github.com/404-Repo/404-active-competition/blob/25f699d926f1bc06c3f9b3f2e0b51b5beea43c8e/rounds/50/prompts.txt) |
| - [Generated JavaScript manifest](https://github.com/404-Repo/404-active-competition/blob/25f699d926f1bc06c3f9b3f2e0b51b5beea43c8e/rounds/50/5ExhiJkJubyzciUVj6j4CdLGCTqbbmGteCjJszhS14j1JbSa/generated_3.json) |
|
|
| `source_manifest.json` records the pinned GitHub commit, source-file hashes, original image and JavaScript URLs, and per-sample image/code hashes. JavaScript targets are preserved without edits. The `system` and `instruction` columns contain the local training pipeline's bundled SceneCoder prompts; these are training conditioning, not a claim about the original submission's generation prompts. `model` is a source identifier; the original generating model is unknown. `attempts=1` denotes one imported target per image, not the original generation attempt count. |
|
|
| ## Training schema |
|
|
| | Column | Type | Content | |
| | --- | --- | --- | |
| | image | Image | Original reference PNG | |
| | system | string | Bundled SceneCoder system prompt | |
| | instruction | string | Image reconstruction instruction | |
| | code | string | Original `export default function generate(THREE)` module | |
| | stem | string | Unique reference image identifier | |
| | source_url | string | Original reference image URL | |
| | attempts | int32 | One imported target | |
| | model | string | Source submission identifier | |
| |
| ```python |
| from datasets import load_dataset |
|
|
| dataset = load_dataset("Fluxmire/data_round_50", split="train") |
| ``` |
| |
| Compatible with this workspace's `train/train_crucible3d.py`: system prompt, user instruction plus image, assistant JavaScript target. Training computes loss on the assistant response. No validation/test split is supplied. |
|
|
| ## Validation |
|
|
| All 128 images decode successfully; each image is paired by stem with its manifest JavaScript module. All 128 modules passed the repository's JavaScript validator, including static checks, `generate(THREE)` execution, and scene validation. See `validation_report.json`. These checks do not measure visual similarity to the reference images. |
|
|
| Source materials retain their original terms; no additional license is asserted here. |
|
|
