Update Links (tusen-ai#239)

* Update INSTALL.md

* Update README.md

* Update README.md

* Update DATASET.md

Author: RogerChern

doc/DATASET.md

@@ -57,7 +57,7 @@ pushd data/src
 
 # download and extract clipart.zip
 # courtesy to "Towards Universal Object Detection by Domain Attention"
-wget http://simpledet.alarge.space:1234/?/clipart.zip -O clipart.zip
+wget https://1dv.alarge.space/clipart.zip -O clipart.zip
 unzip clipart.zip
 popd
 

doc/INSTALL.md

@@ -1,11 +1,11 @@
 ## Setup Locally with Pre-built Wheel
 We provide pre-built wheel for python >= 3.4, Ubuntu >= 14.04 or CentOS >=7. The wheels are staticly linked so no dependency other than CUDA is required.
 
-[Downdload wheel for CUDA-9.0](http://simpledet.alarge.space:1234/mxnet_cu90-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl)
+[Downdload wheel for CUDA-9.0](https://1dv.alarge.space/mxnet_cu90-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl)
 
-[Downdload wheel for CUDA-10.0](http://simpledet.alarge.space:1234/mxnet_cu100-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl)
+[Downdload wheel for CUDA-10.0](https://1dv.alarge.space/mxnet_cu100-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl)
 
-[Downdload wheel for CUDA-10.1](http://simpledet.alarge.space:1234/mxnet_cu101-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl)
+[Downdload wheel for CUDA-10.1](https://1dv.alarge.space/mxnet_cu101-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl)
 
 Install the wheel as
 ```bash
@@ -53,7 +53,7 @@ sudo make install
 
 #### Download singularity image for SimpleDet
 ```bash
-wget http://simpledet.alarge.space:1234/simpledet.img
+wget https://1dv.alarge.space/simpledet.img
 ```
 
 #### Invoke simpledet shell

models/NASFPN/README.md

@@ -23,9 +23,9 @@ All AP results are reported on test-dev of the [COCO dataset](http://cocodataset
 |-----|-----|--------|----|--------------|---|---------|----|---------|-----------|---------------|----|
 |RetinaNet|640|R50v1b-FPN|1@256|25 epoch|8X 1080Ti|8|yes|6.6G|85 img/s|37.4|[model](https://simpledet-model.oss-cn-beijing.aliyuncs.com/retina_r50v1b_fpn_640640_25epoch.zip)|
 |NAS-FPN|640|R50v1b-FPN|7@256|25 epoch|8X 1080Ti|8|yes|7.8G|66 img/s|40.1|[model](https://simpledet-model.oss-cn-beijing.aliyuncs.com/retina_r50v1b_nasfpn_640640_25epoch.zip)|
-|NAS-FPN|1024|R50v1b-FPN|7@256|25 epoch|8X 1080Ti|4|yes|9.1G|17 img/s|44.2|[model](http://simpledet.alarge.space:1234/?/retina_r50v1b_nasfpn_1024_7%40256_25epoch.zip)|
-|NAS-FPN|1280|R50v1b-FPN|7@384|25 epoch|8X 1080Ti|2|yes|8.9G|10 img/s|45.3|[model](http://simpledet.alarge.space:1234/?/retina_r50v1b_nasfpn_1280_7%40384_25epoch.zip)|
-|TD-BU*|1280|R50v1b-FPN|3@384|25 epoch|8X 1080Ti|3|yes|10.5G|12 img/s|44.7|[model](http://simpledet.alarge.space:1234/?/retina_r50v1b_tdbu_1280_3%40384_25epoch.zip)|
+|NAS-FPN|1024|R50v1b-FPN|7@256|25 epoch|8X 1080Ti|4|yes|9.1G|17 img/s|44.2|[model](https://1dv.alarge.space/retina_r50v1b_nasfpn_1024_7%40256_25epoch.zip)|
+|NAS-FPN|1280|R50v1b-FPN|7@384|25 epoch|8X 1080Ti|2|yes|8.9G|10 img/s|45.3|[model](https://1dv.alarge.space/retina_r50v1b_nasfpn_1280_7%40384_25epoch.zip)|
+|TD-BU*|1280|R50v1b-FPN|3@384|25 epoch|8X 1080Ti|3|yes|10.5G|12 img/s|44.7|[model](https://1dv.alarge.space/retina_r50v1b_tdbu_1280_3%40384_25epoch.zip)|
 
 \* Short for TopDown-BottomUp neck which is highly symmetric proposed by Zehao.
 ### Reference

models/efficientnet/README.md

@@ -12,7 +12,7 @@ All AP results are reported on minival of the [COCO dataset](http://cocodataset.
 
 |Model|InputSize|Backbone|Train Schedule|GPU|Image/GPU|FP16|Train MEM|Train Speed|Box AP|Link|
 |-----|-----|--------|--------------|---|---------|----|---------|-----------|---------------|----|
-|Faster|400x600|B5-FPN|36 epoch(6X)|8X 1080Ti|8|yes|-|75 img/s|37.2|[model](http://simpledet.alarge.space:1234/?/efficientnet_b5_fpn_bn_scratch_400_6x.zip)|
+|Faster|400x600|B5-FPN|36 epoch(6X)|8X 1080Ti|8|yes|-|75 img/s|37.2|[model](https://1dv.alarge.space/efficientnet_b5_fpn_bn_scratch_400_6x.zip)|
 |Faster|400x600|B5-FPN|54 epoch(9X)|8X 1080Ti|8|yes|-|75 img/s|37.9|-|
 |Faster|400x600|B5-FPN|72 epoch(12X)|8X 1080Ti|8|yes|-|75 img/s|38.3|-|