| --- |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - image-to-text |
| - table-question-answering |
| - table-to-text |
| tags: |
| - synthetic |
| - chart |
| configs: |
| - config_name: encoder_training |
| data_files: |
| - split: train |
| path: hf_ready/encoder_training/data/train-*.parquet |
| - split: test |
| path: hf_ready/encoder_training/data/test-*.parquet |
| - config_name: decoder_training |
| data_files: |
| - split: train |
| path: hf_ready/decoder_training/data/train-*.parquet |
| - split: test |
| path: hf_ready/decoder_training/data/test-*.parquet |
| - config_name: pipeline_testing |
| data_files: |
| - split: test |
| path: hf_ready/pipeline_testing/data/test-*.parquet |
| --- |
| |
| # Bar Chart Annotation Dataset |
|
|
| This dataset accompanies the paper [Bar-JEPA: Extracting Values from Bar Chart with Joint-Embedding Predictive Architecture](https://huggingface.co/papers/2608.06062). Code is available at [https://github.com/dralois/Bar-JEPA](https://github.com/dralois/Bar-JEPA). |
|
|
| A collection of synthetic bar chart images with detailed structural annotations. Split into three subsets for different stages of a chart understanding pipeline. |
|
|
| --- |
|
|
| ## Subsets |
|
|
| | Subset | Folder | Train | Test | |
| |---|---|---|---| |
| | `encoder_training` | `hf_ready/encoder_training/` | 100,000 | 100 | |
| | `decoder_training` | `hf_ready/decoder_training/` | 17,000 | 3,400 | |
| | `pipeline_testing` | `hf_ready/pipeline_testing/` | — | 100 | |
|
|
| Each subset is a standalone HuggingFace dataset (its own repo), formatted as sharded Parquet files under a `data/` subfolder. |
|
|
| --- |
|
|
| ## Columns |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `filename` | `string` | Original file stem (e.g. `train_42`) — use to reconstruct or cross-reference source files | |
| | `image` | `binary` | Original PNG bytes (RGBA, 1272×954) | |
| | `annotation` | `string` | Verbatim JSON annotation — identical to the source `.json` files | |
| | `chart_type` | `string` | `"vertical"` or `"horizontal"` | |
| | `title` | `string` | Chart title text | |
| | `num_features` | `int32` | Number of data features (legend entries) | |
| | `num_categories` | `int32` | Number of category-axis ticks | |
| | `value_min` | `float32` | Value axis minimum | |
| | `value_max` | `float32` | Value axis maximum | |
|
|
| --- |
|
|
| ## Loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("<org>/encoder_training") |
| sample = ds["train"][0] |
| |
| # Image |
| from PIL import Image |
| from io import BytesIO |
| img = Image.open(BytesIO(sample["image"])) |
| |
| # Annotation (full structure, unchanged) |
| import json |
| ann = json.loads(sample["annotation"]) |
| # ann["chart_metadata"] — title, type, bar_width, legend, origin, size |
| # ann["data"] — category_axis, value_axis, features |
| ``` |
|
|
| --- |
|
|
| ## Annotation Format |
|
|
| Each annotation follows this schema (see `format.json` for the full JSON Schema): |
|
|
| ``` |
| { |
| "chart_metadata": { |
| "title": { "text": "...", "bbox": [x1, y1, x2, y2] }, |
| "type": "vertical" | "horizontal", |
| "bar_width": <int>, |
| "legend": { |
| "bbox": [...], |
| "entries": [ |
| { "feature": { "text": "...", "bbox": [...] }, |
| "patch": { "bbox": [...] } } |
| ] |
| }, |
| "origin": { "bbox": [...] }, |
| "size": { "bbox": [...] } |
| }, |
| "data": { |
| "category_axis": { |
| "label": { "text": "...", "bbox": [...] }, |
| "bbox": [...], |
| "ticks": [ { "label": { "text": "...", "bbox": [...] }, "bbox": [...] } ] |
| }, |
| "value_axis": { |
| "label": { "text": "...", "bbox": [...] }, |
| "min_value": <number>, |
| "max_value": <number>, |
| "bbox": [...], |
| "ticks": [ { "label": { "text": "...", "bbox": [...] }, "bbox": [...] } ] |
| }, |
| "features": [ |
| { |
| "feature": "...", |
| "data": [ |
| { "category": "...", "value": <number>, "bbox": [...] } |
| ] |
| } |
| ] |
| } |
| } |
| ``` |
|
|
| All bounding boxes are `[x1, y1, x2, y2]` in pixel coordinates. |
