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wangsen
MinerU
Commits
f1ae0afc
Commit
f1ae0afc
authored
Jul 23, 2025
by
myhloli
Browse files
docs: update changelog for version 2.1.3 with bug fixes and improvements
parent
e4639e13
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README.md
README.md
+1
-1
README_zh-CN.md
README_zh-CN.md
+1
-1
mineru/model/mfr/unimernet/Unimernet.py
mineru/model/mfr/unimernet/Unimernet.py
+4
-0
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README.md
View file @
f1ae0afc
...
@@ -43,7 +43,7 @@
...
@@ -43,7 +43,7 @@
</div>
</div>
# Changelog
# Changelog
-
2025/07/22 2.1.
2
Released
-
2025/07/22 2.1.
3
Released
-
Bug Fixes
-
Bug Fixes
-
Fixed the issue of excessive memory consumption during the
`MFR`
step in the
`pipeline`
backend under certain scenarios #2771
-
Fixed the issue of excessive memory consumption during the
`MFR`
step in the
`pipeline`
backend under certain scenarios #2771
-
Fixed the inaccurate matching between
`image`
/
`table`
and
`caption`
/
`footnote`
under certain conditions #3129
-
Fixed the inaccurate matching between
`image`
/
`table`
and
`caption`
/
`footnote`
under certain conditions #3129
...
...
README_zh-CN.md
View file @
f1ae0afc
...
@@ -43,7 +43,7 @@
...
@@ -43,7 +43,7 @@
</div>
</div>
# 更新记录
# 更新记录
-
2025/07/22 2.1.
2
发布
-
2025/07/22 2.1.
3
发布
-
bug修复
-
bug修复
-
修复
`pipeline`
后端中
`MFR`
步骤在某些情况下显存消耗过大的问题 #2771
-
修复
`pipeline`
后端中
`MFR`
步骤在某些情况下显存消耗过大的问题 #2771
-
修复某些情况下
`image`
/
`table`
与
`caption`
/
`footnote`
匹配不准确的问题 #3129
-
修复某些情况下
`image`
/
`table`
与
`caption`
/
`footnote`
匹配不准确的问题 #3129
...
...
mineru/model/mfr/unimernet/Unimernet.py
View file @
f1ae0afc
...
@@ -104,6 +104,10 @@ class UnimernetModel(object):
...
@@ -104,6 +104,10 @@ class UnimernetModel(object):
# Create dataset with sorted images
# Create dataset with sorted images
dataset
=
MathDataset
(
sorted_images
,
transform
=
self
.
model
.
transform
)
dataset
=
MathDataset
(
sorted_images
,
transform
=
self
.
model
.
transform
)
# 如果batch_size> len(sorted_images),则设置为不超过len(sorted_images)的2的阶乘
batch_size
=
min
(
batch_size
,
2
**
(
len
(
sorted_images
).
bit_length
()
-
1
))
dataloader
=
DataLoader
(
dataset
,
batch_size
=
batch_size
,
num_workers
=
0
)
dataloader
=
DataLoader
(
dataset
,
batch_size
=
batch_size
,
num_workers
=
0
)
# Process batches and store results
# Process batches and store results
...
...
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