Datasets:

albert13200
/

Visual_Agent_Parquet

license: cc-by-nc-2.0
configs:
  - config_name: samples
    data_files:
      - split: train
        path: samples/*.parquet
  - config_name: images
    data_files:
      - split: train
        path: images/*.parquet
  - config_name: synthetic_attribute_samples
    data_files:
      - split: first_1000
        path: synthetic_attribute/samples/first_1000.parquet
      - split: remaining_4000
        path: synthetic_attribute/samples/remaining_4000.parquet
  - config_name: synthetic_attribute_images
    data_files:
      - split: first_1000
        path: synthetic_attribute/images/first_1000/*.parquet
      - split: remaining_4000
        path: synthetic_attribute/images/remaining_4000/*.parquet

Visual_Agent Parquet

Parquet distribution of albert13200/Visual_Agent with 3679 tool-use trajectories and 4816 deduplicated images.

Tables

samples: one row per trajectory. record_json preserves the complete original row.
images: one row per unique relative image path, with binary bytes, MIME type, dimensions, file size, and SHA-256.
synthetic_attribute_samples: 5,000 synthetic SAM3 crop-replay attribute trajectories, kept separate as first_1000 and remaining_4000 splits.
synthetic_attribute_images: the 10,000 referenced synthetic input/crop images, partitioned to match the two synthetic sample splits.

Separating the tables prevents the same image bytes from being embedded repeatedly when multiple trajectories reference one image.

Use directly

from datasets import load_dataset

samples = load_dataset("albert13200/Visual_Agent_Parquet", "samples", split="train")
images = load_dataset("albert13200/Visual_Agent_Parquet", "images", split="train")

synthetic_first = load_dataset(
    "albert13200/Visual_Agent_Parquet",
    "synthetic_attribute_samples",
    split="first_1000",
)
synthetic_remaining = load_dataset(
    "albert13200/Visual_Agent_Parquet",
    "synthetic_attribute_samples",
    split="remaining_4000",
)

Restore the path-based training layout

uvx --from huggingface-hub hf download \
  albert13200/Visual_Agent_Parquet \
  --repo-type dataset \
  --local-dir Visual_Agent_Parquet

uv run --with pyarrow \
  Visual_Agent_Parquet/materialize_visual_agent_parquet.py \
  --input-root Visual_Agent_Parquet \
  --output-root Visual_Agent

This writes Visual_Agent/training_trajectories_natural/ with the combined JSONL and all relative image files expected by the existing training pipeline. Image bytes are validated against their stored SHA-256 before use.

Restore the synthetic attribute set

uv run --with pyarrow \
  Visual_Agent_Parquet/synthetic_attribute/materialize_synthetic_attribute_parquet.py \
  --input-root Visual_Agent_Parquet/synthetic_attribute \
  --output-root Synthetic_Attribute

Use --partition first_1000 or --partition remaining_4000 to restore only one synthetic partition. Without --partition, the script restores all 5,000 rows and verifies that the combined JSONL exactly matches the source SHA-256.