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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.
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