附表1
模型和数据
为了方便用户使用,我们收集了深度学习常用的数据集,以及一些常用模型的预训练权重,放在对象存储中,用户可直接使用这些数据开始自己的工作,节省下载数据的时间,提高工作效率。
数据集
ImageNet
名称 | 地址 | URL | 尺寸 |
---|---|---|---|
ILSVRC2017 Object localization dataset |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/imagenet/ILSVRC2017_CLS-LOC.tar.gz |
155GB |
|
ILSVRC2017 Object detection dataset |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/imagenet/ILSVRC2017_DET.tar.gz |
55GB |
|
ILSVRC2017 Object detection test dataset |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/imagenet/ILSVRC2017_DET_test_new.tar.gz |
428MB |
COCO
名称 | 地址 | 数量/尺寸 |
---|---|---|
2017 Train Images |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/train2017.zip |
118K/18GB |
2017 Val images |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/val2017.zip |
5K/1GB |
2017 Test images |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/test2017.zip |
41K/6GB |
2017 Unlabeled images |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/unlabeled2017.zip |
123K/19GB |
2017 Train/Val annotations |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/annotations_trainval2017.zip |
241MB |
2017 Stuff Train/Val annotations |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/stuff_annotations_trainval2017.zip |
401MB |
2017 Testing Image info |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/image_info_test2017.zip |
1MB |
2017 Unlabeled Image info |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/coco/image_info_unlabeled2017.zip |
4MB |
PASCAL VOC
OpenSLR
Name | Category | Summary | Files |
---|---|---|---|
Vystadial |
Speech |
English and Czech data, mirrored from the Vystadial project |
|
TED-LIUM |
Speech |
English speech recognition training corpus from TED talks, created by Laboratoire d’Informatique de l’Université du Maine (LIUM) (mirrored here) |
|
THCHS-30 |
Speech |
A Free Chinese Speech Corpus Released by CSLT@Tsinghua University |
data_thchs30.tgz [6.4G]test-noise.tgz [1.9G]resource.tgz [24M] |
Aishell |
Speech |
Mandarin data, provided by Beijing Shell Shell Technology Co.,Ltd |
|
Free ST Chinese Mandarin Corpus |
Speech |
A free Chinese Mandarin corpus by Surfingtech (www.surfing.ai), containing utterances from 855 speakers, 102600 utterances; |
VGGFace2
名称 | 描述 | 地址 | 尺寸 |
---|---|---|---|
Licence.txt |
Licence for VGGFace2 dataset. |
- |
|
Readme.txt |
README. |
- |
|
Vggface2_train.tar.gz |
36G. Loosely cropped faces for training. |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/vggface2/vggface2_train.tar.gz |
36GB |
Vggface2_test.tar.gz |
1.9G. Loosely cropped faces for testing. |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/vggface2/vggface2_test.tar.gz |
1.9GB |
MD5 |
MD5. |
- |
|
Meta.tar.gz |
Meta information for VGGFace2 Dataset. |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/vggface2/meta.tar.gz |
9MB |
BB_Landmark.tar.gz |
The information for bounding boxes and 5 facial landmarks referring to the loosely cropped faces. |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/vggface2/bb_landmark.tar.gz |
170MB |
Dev_kit.tar.gz |
Development kit. |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/vggface2/dev_kit.tar.gz |
3kB |
中英文维基百科语料
名称 | 描述 | 地址 | 尺寸 |
---|---|---|---|
zhwiki-latest-pages-articles.xml.bz2 |
2018年7月23日时最新的中文维基百科语料 |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/wiki/zhwiki-latest-pages-articles.xml.bz2 |
1.5GB |
enwiki-latest-pages-articles.xml.bz2 |
2018年7月23日时最新的英文维基百科语料 |
https://appcenter-deeplearning.sh1a.qingstor.com/dataset/wiki/enwiki-latest-pages-articles.xml.bz2 |
14.2GB |
预训练模型
TensorFlow-Slim image classification model library
下表中 Checkpoint 地址均为山河对象存储地址,可直接下载。
Model | TF-Slim File | Checkpoint | Top-1 Accuracy | Top-5 Accuracy |
---|---|---|---|---|
69.8 |
89.6 |
|||
73.9 |
91.8 |
|||
78.0 |
93.9 |
|||
80.2 |
95.2 |
|||
80.4 |
95.3 |
|||
75.2 |
92.2 |
|||
76.4 |
92.9 |
|||
76.8 |
93.2 |
|||
75.6 |
92.8 |
|||
77.0 |
93.7 |
|||
77.8 |
94.1 |
|||
71.5 |
89.8 |
|||
71.1 |
89.8 |
|||
70.9 |
89.9 |
|||
59.1 |
81.9 |
|||
41.5 |
66.3 |
|||
74.9 |
92.5 |
|||
71.9 |
91.0 |
|||
74.0 |
91.6 |
|||
82.7 |
96.2 |
|||
82.9 |
96.2 |