File size: 3,000 Bytes
f08526f d23fd86 f08526f d23fd86 f08526f d23fd86 f08526f d23fd86 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | ---
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
```python
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
```bash
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
```bash
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.
|