Update links to trained models and added user-pays google cloud stora…

…ge download links

README.md

@@ -48,6 +48,23 @@ For ease-of-use, we make pretrained features available for the entire [MSCOCO da
 - [2014 Testing Image Features (40K / 9GB)](https://imagecaption.blob.core.windows.net/imagecaption/test2014_36.zip)
 - [2015 Testing Image Features (80K / 17GB)](https://imagecaption.blob.core.windows.net/imagecaption/test2015_36.zip)
 
+
+If the above links are too slow, the features can be downloaded from Google Cloud Storage at own cost using the Requester Pays feature. Use the following gsutil commands where [PROJECT_ID] is your Cloud Storage project id that should be billed (further documentation can be found [here](https://cloud.google.com/storage/docs/using-requester-pays):
+
+10 to 100 features per image (adaptive):	10 to 100 features per image (adaptive):
+```
+gsutil -u [PROJECT_ID] cp gs://bottom-up-attention/trainval.zip [OBJECT_DESTINATION] # 2014 Train/Val Image Features (120K / 23GB)
+gsutil -u [PROJECT_ID] cp gs://bottom-up-attention/test2014.zip [OBJECT_DESTINATION] # 2014 Testing Image Features (40K / 7.3GB)
+gsutil -u [PROJECT_ID] cp gs://bottom-up-attention/test2015.zip [OBJECT_DESTINATION] # 2015 Testing Image Features (80K / 15GB)
+```
+
+36 features per image (fixed):	36 features per image (fixed):
+```
+gsutil -u [PROJECT_ID] cp gs://bottom-up-attention/trainval_36.zip [OBJECT_DESTINATION] # 2014 Train/Val Image Features (120K / 25GB)
+gsutil -u [PROJECT_ID] cp gs://bottom-up-attention/test2014_36.zip [OBJECT_DESTINATION] # 2014 Testing Image Features (40K / 9GB)
+gsutil -u [PROJECT_ID] cp gs://bottom-up-attention/test2015_36.zip [OBJECT_DESTINATION] # 2015 Testing Image Features (80K / 17GB)
+```
+
 Both sets of features can be recreated by using `tools/genenerate_tsv.py` with the appropriate pretrained model and with MIN_BOXES/MAX_BOXES set to either 10/100 or 36/36 respectively - refer [Demo](#demo). 
 
 ### Contents
@@ -105,11 +122,11 @@ Any NVIDIA GPU with 12GB or larger memory is OK for training Faster R-CNN ResNet
 
 ### Demo
 
-1.  Download [pretrained model](https://storage.googleapis.com/bottom-up-attention/resnet101_faster_rcnn_final.caffemodel), and put it under `data\faster_rcnn_models`.
+1.  Download [pretrained model](https://www.dropbox.com/s/tr24q7h0zm2wnjv/resnet101_faster_rcnn_final.caffemodel?dl=1), and put it under `data\faster_rcnn_models`.
 
 2.  Run `tools/demo.ipynb` to show object and attribute detections on demo images.
 
-3.  Run `tools/genenerate_tsv.py` to extract bounding box features to a tab-separated-values (tsv) file. This will require modifying the `load_image_ids` function to suit your data locations. To recreate the pretrained feature files with 10 to 100 features per image, set MIN_BOXES=10 and MAX_BOXES=100. To recreate the pretrained feature files with 36 features per image, set MIN_BOXES=36 and MAX_BOXES=36 use this [alternative pretrained model](https://storage.googleapis.com/bottom-up-attention/resnet101_faster_rcnn_final_iter_320000.caffemodel) instead. The alternative pretrained model was trained for fewer iterations but performance is similar.
+3.  Run `tools/genenerate_tsv.py` to extract bounding box features to a tab-separated-values (tsv) file. This will require modifying the `load_image_ids` function to suit your data locations. To recreate the pretrained feature files with 10 to 100 features per image, set MIN_BOXES=10 and MAX_BOXES=100. To recreate the pretrained feature files with 36 features per image, set MIN_BOXES=36 and MAX_BOXES=36 use this [alternative pretrained model](https://www.dropbox.com/s/bacig173qnxddvz/resnet101_faster_rcnn_final_iter_320000.caffemodel?dl=1) instead. The alternative pretrained model was trained for fewer iterations but performance is similar.
 
 
 ### Training