File size: 4,094 Bytes
4a3a9ec e9da2cd 4a3a9ec |
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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
---
license: cc
task_categories:
- visual-question-answering
language:
- en
tags:
- plotQA
pretty_name: PlotQA
---
# Dataset Card for PlotQA
## Dataset Description
- **PlotQA from here:** [PlotQA](https://github.com/NiteshMethani/PlotQA)
### Dataset Summary
PlotQA is a VQA dataset with 28.9 million question-answer pairs grounded over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates.
## Dataset Structure
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `image`: PIL image of a plot
- `text`: string of json data 'models'. See notes below.
From [here](https://github.com/NiteshMethani/PlotQA/blob/master/PlotQA_Dataset.md):
'models': It is a list of dictionaries. Depending on the type of the plot (single or 2,3,4-multi), the length of the dictionary can vary from 1 to 4. Each dictionary contains the following keys-
name: Label corresponding to the datapoint.
color: Color corresponding to the `name` datapoint.
bboxes: Bounding boxes corresponding to the `name` datapoints in the plot.
label: label corresponding to the datapoint which will appear as the legend (same as the `name` field).
x: x-value of the datapoints.
y: y-value of the datapoints.
[json2token](https://github.com/clovaai/donut/blob/b317b4bbf1eecec7c62e7666f2097e1e90a6b441/donut/model.py#L495) function was used to convert json to string.
The new tokens are already loaded in plotQA processor:
```
from transformers import DonutProcessor
processor = DonutProcessor.from_pretrained("[achang/donut-plotqa-trained](https://huggingface.co/achang/donut-plotqa-trained)")
```
### Data Splits
```
validation: Dataset({
features: ['image', 'text'],
num_rows: 33650
})
train: Dataset({
features: ['image', 'text'],
num_rows: 157070
})
test: Dataset({
features: ['image', 'text'],
num_rows: 33657
})
```
## Example
Here is example how to use
```
def pre_process(sample, processor, max_length=512, ignore_id=-100):
# create tensor from image
try:
image = sample["image"].convert('RGB')
pixel_values = processor(
image, return_tensors="pt").pixel_values.squeeze()
except Exception as e:
print(sample)
print(f"Error: {e}")
return {}
# tokenize document
input_ids = processor.tokenizer(
sample["text"],
add_special_tokens=False,
max_length=max_length,
padding="max_length",
truncation=True,
return_tensors="pt",
)["input_ids"].squeeze(0)
labels = input_ids.clone()
# model doesn't need to predict pad token
labels[labels == processor.tokenizer.pad_token_id] = ignore_id
return {"pixel_values": pixel_values, "labels": labels, "target_sequence": sample["text"]}
dataset = load_dataset("achang/plot_qa", streaming=True)
processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base")
processed_dataset = dataset.map(partial(pre_process, processor=processor, max_length=512, ignore_id=-100))
```
## Misc
Dataset Creation, Annotations, Considerations for Using the Data, Social Impact of Dataset, Additional Information, Licensing Information look at [plotQA](https://github.com/NiteshMethani/PlotQA)
### Citation Information
Please cite the following if you use the PlotQA dataset in your work:
```
@InProceedings{Methani_2020_WACV,
author = {Methani, Nitesh and Ganguly, Pritha and Khapra, Mitesh M. and Kumar, Pratyush},
title = {PlotQA: Reasoning over Scientific Plots},
booktitle = {The IEEE Winter Conference on Applications of Computer Vision (WACV)},
month = {March},
year = {2020}
}
```
|