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1da285f | 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 | import os
import pandas as pd
METHODS = [
'FasterRCNN',
'CenterNet2',
'YOLO',
'OmniParser',
# 'GPT4V-E2E',
# 'Gemini-E2E',
'CogVLM',
'Seed-E2E',
'internVL-E2E',
'Qwen3-VL-plus-E2E',
'O4-E2E',
'Claude4_5-sonnet-E2E',
'Gemini-2_5-pro-E2E',
'GPT5-E2E',
'aaa_icse_test_set_merged_gpt4v_gpt4v_ape_d_object_bbox_r1s_bf',
'realfse_union3_gpt4v_ape_d_object_bbox_gpu0123_s_bf',
'realfse_union3_claude35sonnet_ape_d_object_bbox_gpu0123_s_bf',
'realfse_union3_gemini15pro_ape_d_object_bbox_gpu0123_s_bf'
]
RESULT_ROOT = '../eval_context/results'
methods_row = {
'row1': METHODS[:8],
'row2': METHODS[8:],
}
def my_format(x):
if x < 0.005:
return r'$\approx$0.0'
return f'{x:.2f}'
for row, methods in methods_row.items():
method_dfs = []
for method in methods:
method_df = pd.DataFrame()
for iou in [0.75, 0.8, 0.85, 0.9, 0.95]:
df = pd.read_csv(os.path.join(RESULT_ROOT, f'{method}@{iou:.2f}.csv'))
df = df[['precision', 'recall', 'f1']] * 100
df = df.iloc[-1:]
method_df = pd.concat([method_df, df], axis=0)
method_dfs.append(method_df)
df = pd.concat(method_dfs, axis=1)
df.insert(0, column='IoU', value=['0.75', '0.80', '0.85', '0.90', '0.95'])
df.to_csv(f'context_{row}.csv', index=False)
df.to_latex(f'context_{row}.tex', index=False, float_format=my_format)
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