dralois/Bar-JEPA
Keypoint Detection • Updated
filename stringlengths 7 11 | image unknown | annotation stringlengths 2.07k 5.78k | chart_type stringclasses 1
value | title stringlengths 0 15 | num_features int32 1 3 | num_categories int32 2 5 | value_min float32 10 10 | value_max float32 50 210 |
|---|---|---|---|---|---|---|---|---|
train_0 | [
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0,
0,
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73,
72,
68,
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83,
111,
102,
116,
119,
97,
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0,
77,
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116,
11... | {
"chart_metadata": {
"title": {
"text": "vdpWX",
"bbox": [608,23,733,58]
},
"type": "vertical",
"bar_width": 100,
"legend": {
"entries": [],
"bbox": [0,0,0,0]
},
"size": {
"bbox": [0,0,1272,954]
},
"origin": {
"bbox": [89,881,97,889]
}
},
... | vertical | vdpWX | 3 | 3 | 10 | 115 |
train_1 | [
137,
80,
78,
71,
13,
10,
26,
10,
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73,
72,
68,
82,
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8,
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0,
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83,
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116,
119,
97,
114,
101,
0,
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116,
11... | {
"chart_metadata": {
"title": {
"text": "cVNvZMf",
"bbox": [114,20,242,49]
},
"type": "vertical",
"bar_width": 37,
"legend": {
"entries": [
{
"feature": {
"text": "bHiA",
"bbox": [955,428,1025,457]
},
"patch": {
... | vertical | cVNvZMf | 2 | 4 | 10 | 143 |
train_2 | [
137,
80,
78,
71,
13,
10,
26,
10,
0,
0,
0,
13,
73,
72,
68,
82,
0,
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110,
0,
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8,
6,
0,
0,
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47,
225,
1,
0,
0,
0,
58,
116,
69,
88,
116,
83,
111,
102,
116,
119,
97,
114,
101,
0,
77,
97,
116,
112,... | {
"chart_metadata": {
"title": {
"text": "U cHT",
"bbox": [972,27,1106,73]
},
"type": "vertical",
"bar_width": 84,
"legend": {
"entries": [],
"bbox": [0,0,0,0]
},
"size": {
"bbox": [0,0,1134,1323]
},
"origin": {
"bbox": [129,1215,138,1224]
}
... | vertical | U cHT | 1 | 3 | 10 | 179 |
train_3 | [
137,
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78,
71,
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10,
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0,
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72,
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112,
... | {
"chart_metadata": {
"title": {
"text": "",
"bbox": [0,0,0,0]
},
"type": "vertical",
"bar_width": 28,
"legend": {
"entries": [
{
"feature": {
"text": "FKkjW",
"bbox": [338,758,412,785]
},
"patch": {
"bbox"... | vertical | 3 | 5 | 10 | 142 | |
train_4 | "iVBORw0KGgoAAAANSUhEUgAABIwAAAVOCAYAAAAQAaKkAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED) | "{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"yukgv okj KIn\",\n \"bbox\": [(...TRUNCATED) | vertical | yukgv okj KIn | 3 | 4 | 10 | 161 |
train_5 | "iVBORw0KGgoAAAANSUhEUgAAAmQAAAMwCAYAAACOeXzpAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED) | "{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"uriYvOoq\",\n \"bbox\": [257,1(...TRUNCATED) | vertical | uriYvOoq | 1 | 2 | 10 | 117 |
train_6 | "iVBORw0KGgoAAAANSUhEUgAAA5gAAAMlCAYAAAAWnFwWAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED) | "{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"\",\n \"bbox\": [0,0,0,0]\n (...TRUNCATED) | vertical | 1 | 5 | 10 | 184 | |
train_7 | "iVBORw0KGgoAAAANSUhEUgAAA2QAAAPgCAYAAABdyb8GAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED) | "{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"\",\n \"bbox\": [0,0,0,0]\n (...TRUNCATED) | vertical | 3 | 3 | 10 | 188 | |
train_8 | "iVBORw0KGgoAAAANSUhEUgAABhgAAAYYCAYAAABrEsKBAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED) | "{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"R pSN\",\n \"bbox\": [758,27,8(...TRUNCATED) | vertical | R pSN | 3 | 4 | 10 | 142 |
train_9 | "iVBORw0KGgoAAAANSUhEUgAABNAAAATQCAYAAAAyMrjlAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjg(...TRUNCATED) | "{\n \"chart_metadata\": {\n \"title\": {\n \"text\": \"DLHjJwjNqn\",\n \"bbox\": [135(...TRUNCATED) | vertical | DLHjJwjNqn | 3 | 4 | 10 | 171 |
A collection of synthetic bar chart images with detailed structural annotations. Split into three subsets for different stages of a chart understanding pipeline.
| 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.
| 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 |
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
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.