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import ast
import argparse
import glob
import pickle
import gradio as gr
import numpy as np
import pandas as pd
block_css = """
#notice_markdown {
font-size: 104%
}
#notice_markdown th {
display: none;
}
#notice_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_markdown {
font-size: 104%
}
#leaderboard_markdown td {
padding-top: 6px;
padding-bottom: 6px;
}
#leaderboard_dataframe td {
line-height: 0.1em;
font-size: 8px;
}
footer {
display:none !important
}
.image-container {
display: flex;
align-items: center;
padding: 1px;
}
.image-container img {
margin: 0 30px;
height: 20px;
max-height: 100%;
width: auto;
max-width: 20%;
}
"""
def model_hyperlink(model_name, link):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
lines = open(filename).readlines()
heads = [v.strip() for v in lines[0].split(",")]
rows = []
for i in range(1, len(lines)):
row = [v.strip() for v in lines[i].split(",")]
for j in range(len(heads)):
item = {}
for h, v in zip(heads, row):
if h != "Model" and h != "Link" and h != "Language Model" and h != "Open Source":
item[h] = float(v)
else:
item[h] = v
if add_hyperlink:
item["Model"] = model_hyperlink(item["Model"], item["Link"])
rows.append(item)
return rows
def get_arena_table(model_table_df):
# sort by rating
model_table_df = model_table_df.sort_values(by=["Average Score"], ascending=False)
values = []
for i in range(len(model_table_df)):
row = []
model_key = model_table_df.index[i]
model_name = model_table_df["Model"].values[model_key]
# rank
row.append(i + 1)
# model display name
row.append(model_name)
# row.append(
# model_table_df["Language Model"].values[model_key]
# )
row.append(
model_table_df["Open Source"].values[model_key]
)
row.append(
model_table_df["Text Recognition"].values[model_key]
)
row.append(
model_table_df["Text Referring"].values[model_key]
)
row.append(
model_table_df["Text Spotting"].values[model_key]
)
row.append(
model_table_df["Relation Extraction"].values[model_key]
)
row.append(
model_table_df["Element Parsing"].values[model_key]
)
row.append(
model_table_df["Mathematical Calculation"].values[model_key]
)
row.append(
model_table_df["Visual Text Understanding"].values[model_key]
)
row.append(
model_table_df["Knowledge Reasoning"].values[model_key]
)
row.append(
model_table_df["Average Score"].values[model_key]
)
values.append(row)
return values
def get_cn_table(model_table_df):
# sort by rating
model_table_df = model_table_df.sort_values(by=["Average Score"], ascending=False)
values = []
for i in range(len(model_table_df)):
row = []
model_key = model_table_df.index[i]
model_name = model_table_df["Model"].values[model_key]
# rank
row.append(i + 1)
# model display name
row.append(model_name)
row.append(
model_table_df["Open Source"].values[model_key]
)
row.append(
model_table_df["Text Recognition"].values[model_key]
)
row.append(
model_table_df["Relation Extraction"].values[model_key]
)
row.append(
model_table_df["Element Parsing"].values[model_key]
)
row.append(
model_table_df["Visual Text Understanding"].values[model_key]
)
row.append(
model_table_df["Knowledge Reasoning"].values[model_key]
)
row.append(
model_table_df["Average Score"].values[model_key]
)
values.append(row)
return values
def build_leaderboard_tab(leaderboard_table_file, leaderboard_table_file_2, show_plot=False):
if leaderboard_table_file:
data = load_leaderboard_table_csv(leaderboard_table_file)
data_2 = load_leaderboard_table_csv(leaderboard_table_file_2)
model_table_df = pd.DataFrame(data)
model_table_df_2 = pd.DataFrame(data_2)
md_head = f"""
# 🏆 OCRBench v2 Leaderboard
| [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR) | [Paper](https://arxiv.org/abs/2305.07895) |
"""
gr.Markdown(md_head, elem_id="leaderboard_markdown")
with gr.Tabs() as tabs:
# arena table
with gr.Tab("OCRBench v2", id=0):
arena_table_vals = get_arena_table(model_table_df)
md = "OCRBench v2 is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"Name",
"Open Source",
"Text Recognition",
"Text Referring",
"Text Spotting",
"Relation Extraction",
"Element Parsing",
"Mathematical Calculation",
"Visual Text Understanding",
"Knowledge Reasoning",
"Average Score",
],
datatype=[
"str",
"markdown",
"str",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
"number",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
wrap=True,
)
with gr.Tab("OCRBench v2 cn", id=1):
arena_table_vals = get_cn_table(model_table_df_2)
md = "OCRBench is a comprehensive evaluation benchmark designed to assess the OCR capabilities of Large Multimodal Models. It comprises five components: Text Recognition, SceneText-Centric VQA, Document-Oriented VQA, Key Information Extraction, and Handwritten Mathematical Expression Recognition. The benchmark includes 1000 question-answer pairs, and all the answers undergo manual verification and correction to ensure a more precise evaluation."
gr.Markdown(md, elem_id="leaderboard_markdown")
gr.Dataframe(
headers=[
"Rank",
"Name",
"Open Source",
"Text Recognition",
"Relation Extraction",
"Element Parsing",
"Visual Text Understanding",
"Knowledge Reasoning",
"Average Score",
],
datatype=[
"str",
"markdown",
"str",
"number",
"number",
"number",
"number",
"number",
"number",
],
value=arena_table_vals,
elem_id="arena_leaderboard_dataframe",
# height=700,
column_widths=[60, 120,150,100, 100, 100, 100, 100, 80],
wrap=True,
)
else:
pass
md_tail = f"""
# Notice
Sometimes, API calls to closed-source models may not succeed. In such cases, we will repeat the calls for unsuccessful samples until it becomes impossible to obtain a successful response. It is important to note that due to rigorous security reviews by OpenAI, GPT4V refuses to provide results for the 84 samples in OCRBench.
If you would like to include your model in the OCRBench leaderboard, please follow the evaluation instructions provided on [GitHub](https://github.com/Yuliang-Liu/MultimodalOCR), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) or [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval) and feel free to contact us via email at [email protected]. We will update the leaderboard in time."""
gr.Markdown(md_tail, elem_id="leaderboard_markdown")
def build_demo(leaderboard_table_file, leaderboard_table_file_2):
text_size = gr.themes.sizes.text_lg
with gr.Blocks(
title="OCRBench Leaderboard",
theme=gr.themes.Base(text_size=text_size),
css=block_css,
) as demo:
leader_components = build_leaderboard_tab(
leaderboard_table_file, leaderboard_table_file_2, show_plot=True
)
return demo
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--share", action="store_true")
parser.add_argument("--OCRBench_file", type=str, default="./OCRBench_en.csv")
parser.add_argument("--OCRBench_file_2", type=str, default="./OCRBench_cn.csv")
args = parser.parse_args()
demo = build_demo(args.OCRBench_file, args.OCRBench_file_2)
demo.launch() |