Datasets:

Fluxmire
/

data_round_50

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

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

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.