Merge pull request #181 from facebookresearch/update_resource_links

updated links to detectron and densepose resources
facebookresearch · Jan 14, 2019 · 35e69d1 · 35e69d1
2 parents 0bb343e + 4c037f1
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diff --git a/DensePoseData/get_DensePose_COCO.sh b/DensePoseData/get_DensePose_COCO.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 mkdir DensePose_COCO
 cd DensePose_COCO
-wget https://s3.amazonaws.com/densepose/densepose_coco_2014_train.json
-wget https://s3.amazonaws.com/densepose/densepose_coco_2014_valminusminival.json
-wget https://s3.amazonaws.com/densepose/densepose_coco_2014_minival.json
-wget https://s3.amazonaws.com/densepose/densepose_coco_2014_test.json
+wget https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_train.json
+wget https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_valminusminival.json
+wget https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_minival.json
+wget https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_test.json
diff --git a/DensePoseData/get_densepose_uv.sh b/DensePoseData/get_densepose_uv.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 mkdir UV_data
 cd UV_data
-wget https://s3.amazonaws.com/densepose/densepose_uv_data.tar.gz
+wget https://dl.fbaipublicfiles.com/densepose/densepose_uv_data.tar.gz
 tar xvf densepose_uv_data.tar.gz
 rm densepose_uv_data.tar.gz
diff --git a/DensePoseData/get_eval_data.sh b/DensePoseData/get_eval_data.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 mkdir eval_data
 cd eval_data
-wget https://s3.amazonaws.com/densepose/densepose_eval_data.tar.gz
+wget https://dl.fbaipublicfiles.com/densepose/densepose_eval_data.tar.gz
 tar xvf densepose_eval_data.tar.gz
 rm densepose_eval_data.tar.gz
diff --git a/GETTING_STARTED.md b/GETTING_STARTED.md
@@ -15,7 +15,7 @@ python2 tools/infer_simple.py \
     --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml \
     --output-dir DensePoseData/infer_out/ \
     --image-ext jpg \
-    --wts https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \
+    --wts https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \
     DensePoseData/demo_data/demo_im.jpg
 ```
 
@@ -30,7 +30,7 @@ This example shows how to run an end-to-end trained DensePose-RCNN model from th
 ```
 python2 tools/test_net.py \
     --cfg configs/DensePose_ResNet101_FPN_s1x-e2e.yaml \
-    TEST.WEIGHTS https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \
+    TEST.WEIGHTS https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl \
     NUM_GPUS 1
 ```
 

diff --git a/MODEL_ZOO.md b/MODEL_ZOO.md
@@ -5,20 +5,20 @@ Herein, we provide RPN files for DensePose-COCO dataset `train`, `minival` and `
 
 The RPN results are obtained using the models provided in the Detectron model-zoo. For performance measures please refer to [`this file`](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#person-specific-rpn-baselines).
 
-**X-101-32x8d-FPN:** [`[train]`](https://s3.amazonaws.com/densepose/DensePose-RPN-train_X-101-32x8d-FPN.pkl) [`[minival]`](https://s3.amazonaws.com/densepose/DensePose-RPN-minival_X-101-32x8d-FPN.pkl) [`[valminusminival]`](https://s3.amazonaws.com/densepose/DensePose-RPN-valminusminival_X-101-32x8d-FPN.pkl)
+**X-101-32x8d-FPN:** [`[train]`](https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-train_X-101-32x8d-FPN.pkl) [`[minival]`](https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-minival_X-101-32x8d-FPN.pkl) [`[valminusminival]`](https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-valminusminival_X-101-32x8d-FPN.pkl)
 
-**R-50-FPN:** [`[train]`](https://s3.amazonaws.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl) [`[minival]`](https://s3.amazonaws.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl) [`[valminusminival]`](https://s3.amazonaws.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl)
+**R-50-FPN:** [`[train]`](https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl) [`[minival]`](https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl) [`[valminusminival]`](https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl)
 
 ## DensePose-RCNN Models
 
 | Model | AP  |  AP50 | AP75  | APm  |APl |
 |-----|-----|---    |---    |---   |--- |
-| [`ResNet50_FPN_s1x`](https://s3.amazonaws.com/densepose/DensePose_ResNet50_FPN_s1x.pkl)| 0.4748 |0.8368|0.4820 |0.4262|0.4948|
-| [`ResNet50_FPN_s1x-e2e`](https://s3.amazonaws.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl)|0.4892 |0.8490|0.5078| 0.4384|0.5059|
-| [`ResNet101_FPN_s1x`](https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x.pkl)|0.4978| 0.8521|0.5276 |0.4373|0.5164|
-| [`ResNet101_FPN_s1x-e2e`](https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl)|0.5147 |0.8660|0.5601 |0.4716|0.5291|
-| [`ResNet101_FPN_32x8d_s1x`](https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_32x8d_s1x.pkl)|0.5095 | 0.8590|0.5381 |0.4605|0.5272|
-| [`ResNet101_FPN_32x8d_s1x-e2e`](https://s3.amazonaws.com/densepose/DensePose_ResNet101_FPN_32x8d_s1x-e2e.pkl)|0.5554 | 0.8908|0.6080 |0.5067|0.5676|
+| [`ResNet50_FPN_s1x`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x.pkl)| 0.4748 |0.8368|0.4820 |0.4262|0.4948|
+| [`ResNet50_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl)|0.4892 |0.8490|0.5078| 0.4384|0.5059|
+| [`ResNet101_FPN_s1x`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x.pkl)|0.4978| 0.8521|0.5276 |0.4373|0.5164|
+| [`ResNet101_FPN_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_s1x-e2e.pkl)|0.5147 |0.8660|0.5601 |0.4716|0.5291|
+| [`ResNet101_FPN_32x8d_s1x`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_32x8d_s1x.pkl)|0.5095 | 0.8590|0.5381 |0.4605|0.5272|
+| [`ResNet101_FPN_32x8d_s1x-e2e`](https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet101_FPN_32x8d_s1x-e2e.pkl)|0.5554 | 0.8908|0.6080 |0.5067|0.5676|
 
 Please note that due to the new per-part normalization the AP numbers do not match those reported in the paper, which are obtained with global normalization factor `K = 0.255`.
 
@@ -40,5 +40,5 @@ independent models for individual tasks.
 | densepose | 0.5075 | 0.8606 | 0.5373 | 0.4356 | 0.5265 |
 
 ([config](configs/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml),
-[model](https://s3.amazonaws.com/densepose/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.pkl),
-[md5](https://s3.amazonaws.com/densepose/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.md5))
+[model](https://dl.fbaipublicfiles.com/densepose/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.pkl),
+[md5](https://dl.fbaipublicfiles.com/densepose/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.pkl.md5sum))
diff --git a/PoseTrack/README.md b/PoseTrack/README.md
@@ -58,7 +58,7 @@ To demonstrate the evaluation, we use a DensePose-RCNN with a ResNet-50 trunk th
 cd $DENSEPOSE
 python2 tools/test_net.py \
     --cfg PoseTrack/configs/DensePose_ResNet50_FPN_s1x-e2e.yaml \
-    TEST.WEIGHTS https://s3.amazonaws.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl \
+    TEST.WEIGHTS https://dl.fbaipublicfiles.com/densepose/DensePose_ResNet50_FPN_s1x-e2e.pkl \
     NUM_GPUS 1
 ```
 The evaluation of this baseline network should yield `Bounding Box AP: 0.4438` and `DensePose AP: 0.2698`.
diff --git a/PoseTrack/configs/DensePose_ResNet50_FPN_s1x-e2e.yaml b/PoseTrack/configs/DensePose_ResNet50_FPN_s1x-e2e.yaml
@@ -59,7 +59,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333

diff --git a/challenge/2018_COCO_DensePose/readme.md b/challenge/2018_COCO_DensePose/readme.md
@@ -23,11 +23,11 @@ The COCO train, validation, and test sets, containing more than 39,000 images
 and 56,000 person instances labeled with DensePose annotations are available
 for [download](http://cocodataset.org/#download).
 Annotations on train (
-[train 1](https://s3.amazonaws.com/densepose/densepose_coco_2014_train.json),
-[train 2](https://s3.amazonaws.com/densepose/densepose_coco_2014_valminusminival.json)
-) and [val](https://s3.amazonaws.com/densepose/densepose_coco_2014_minival.json)
+[train 1](https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_train.json),
+[train 2](https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_valminusminival.json)
+) and [val](https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_minival.json)
 with over 48,000 people are publicly available.
-[Test set](https://s3.amazonaws.com/densepose/densepose_coco_2014_test.json)
+[Test set](https://dl.fbaipublicfiles.com/densepose/densepose_coco_2014_test.json)
 with the list of images is also available for download.
 
 Evaluation server for the 2018 task is

diff --git a/configs/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml b/configs/DensePoseKeyPointsMask_ResNet50_FPN_s1x-e2e.yaml
@@ -82,7 +82,7 @@ KRCNN:
   ROI_XFORM_SAMPLING_RATIO: 2
   KEYPOINT_CONFIDENCE: bbox
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333

diff --git a/configs/DensePose_ResNet101_FPN.yaml b/configs/DensePose_ResNet101_FPN.yaml
@@ -58,7 +58,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   PROPOSAL_FILES: ('dense_coco_2014_train.pkl','dense_coco_2014_valminusminival.pkl')
   SCALES: (640, 672, 704, 736, 768, 800)

diff --git a/configs/DensePose_ResNet101_FPN_32x8d_s1x-e2e.yaml b/configs/DensePose_ResNet101_FPN_32x8d_s1x-e2e.yaml
@@ -66,7 +66,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333

diff --git a/configs/DensePose_ResNet101_FPN_32x8d_s1x.yaml b/configs/DensePose_ResNet101_FPN_32x8d_s1x.yaml
@@ -65,7 +65,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   PROPOSAL_FILES: ('dense_coco_2014_train.pkl','dense_coco_2014_valminusminival.pkl')
   SCALES: (640, 672, 704, 736, 768, 800)

diff --git a/configs/DensePose_ResNet101_FPN_s1x-e2e.yaml b/configs/DensePose_ResNet101_FPN_s1x-e2e.yaml
@@ -59,7 +59,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333

diff --git a/configs/DensePose_ResNet101_FPN_s1x.yaml b/configs/DensePose_ResNet101_FPN_s1x.yaml
@@ -58,17 +58,17 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-101.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
-  PROPOSAL_FILES: ('https://s3.amazonaws.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl','https://s3.amazonaws.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl')
+  PROPOSAL_FILES: ('https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl','https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333
   IMS_PER_BATCH: 3
   BATCH_SIZE_PER_IM: 512
   USE_FLIPPED: True
 TEST:
   DATASETS: ('dense_coco_2014_minival',)
-  PROPOSAL_FILES: ('https://s3.amazonaws.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl',)
+  PROPOSAL_FILES: ('https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl',)
   PROPOSAL_LIMIT: 1000
   SCALE: 800
   MAX_SIZE: 1333

diff --git a/configs/DensePose_ResNet50_FPN.yaml b/configs/DensePose_ResNet50_FPN.yaml
@@ -58,7 +58,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   PROPOSAL_FILES: ('dense_coco_2014_train.pkl','dense_coco_2014_valminusminival.pkl')
   SCALES: (640, 672, 704, 736, 768, 800)

diff --git a/configs/DensePose_ResNet50_FPN_s1x-e2e.yaml b/configs/DensePose_ResNet50_FPN_s1x-e2e.yaml
@@ -59,7 +59,7 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333

diff --git a/configs/DensePose_ResNet50_FPN_s1x.yaml b/configs/DensePose_ResNet50_FPN_s1x.yaml
@@ -58,17 +58,17 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
-  PROPOSAL_FILES: ('https://s3.amazonaws.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl','https://s3.amazonaws.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl')
+  PROPOSAL_FILES: ('https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl','https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333
   IMS_PER_BATCH: 3
   BATCH_SIZE_PER_IM: 512
   USE_FLIPPED: True
 TEST:
   DATASETS: ('dense_coco_2014_minival',)
-  PROPOSAL_FILES: ('https://s3.amazonaws.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl',)
+  PROPOSAL_FILES: ('https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl',)
   PROPOSAL_LIMIT: 1000
   SCALE: 800
   MAX_SIZE: 1333

diff --git a/configs/DensePose_ResNet50_FPN_single_GPU.yaml b/configs/DensePose_ResNet50_FPN_single_GPU.yaml
@@ -58,17 +58,17 @@ BODY_UV_RCNN:
   ##
   BODY_UV_IMS: True 
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('dense_coco_2014_train', 'dense_coco_2014_valminusminival')
-  PROPOSAL_FILES: ('https://s3.amazonaws.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl','https://s3.amazonaws.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl')
+  PROPOSAL_FILES: ('https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-train_fpn_resnet50.pkl','https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-valminusminival_fpn_resnet50.pkl')
   SCALES: (640, 672, 704, 736, 768, 800)
   MAX_SIZE: 1333
   IMS_PER_BATCH: 3
   BATCH_SIZE_PER_IM: 512
   USE_FLIPPED: True
 TEST:
   DATASETS: ('dense_coco_2014_minival',)
-  PROPOSAL_FILES: ('https://s3.amazonaws.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl',)
+  PROPOSAL_FILES: ('https://dl.fbaipublicfiles.com/densepose/DensePose-RPN-minival_fpn_resnet50.pkl',)
   PROPOSAL_LIMIT: 1000
   SCALE: 800
   MAX_SIZE: 1333

diff --git a/configs/rpn_densepose_only_R-50-FPN_1x.yaml b/configs/rpn_densepose_only_R-50-FPN_1x.yaml
@@ -19,12 +19,12 @@ FPN:
   RPN_ANCHOR_START_SIZE: 32
   RPN_ASPECT_RATIOS: (0.5, 1, 2)
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
   DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
   SCALES: (800,)
   MAX_SIZE: 1333
 TEST:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/train/keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/35998996/12_2017_baselines/rpn_person_only_R-50-FPN_1x.yaml.08_10_08.0ZWmJm6F/output/train/keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
   DATASETS: ('dense_coco_2014_minival', 'dense_coco_2014_train', 'dense_coco_2014_valminusminival')
   SCALE: 800
   MAX_SIZE: 1333

diff --git a/configs/rpn_densepose_only_X-101-32x8d-FPN_1x.yaml b/configs/rpn_densepose_only_X-101-32x8d-FPN_1x.yaml
@@ -24,12 +24,12 @@ RESNETS:
   NUM_GROUPS: 32
   WIDTH_PER_GROUP: 8
 TRAIN:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/20171220/X-101-32x8d.pkl
   DATASETS: ('keypoints_coco_2014_train', 'keypoints_coco_2014_valminusminival')
   SCALES: (800,)
   MAX_SIZE: 1333
 TEST:
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/train/keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
+  WEIGHTS: https://dl.fbaipublicfiles.com/detectron/36760438/12_2017_baselines/rpn_person_only_X-101-32x8d-FPN_1x.yaml.06_04_23.M2oJlDPW/output/train/keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
   DATASETS: ( 'dense_coco_2014_minival', 'dense_coco_2014_valminusminival')
   SCALE: 800
   MAX_SIZE: 1333

diff --git a/detectron/datasets/data/README.md b/detectron/datasets/data/README.md
@@ -36,7 +36,7 @@ ln -s /path/to/json/annotations $DETECTRON/detectron/datasets/data/coco/annotati
 
 ### COCO Minival Annotations
 
-Our custom `minival` and `valminusminival` annotations are available for download [here](https://s3-us-west-2.amazonaws.com/detectron/coco/coco_annotations_minival.tgz).
+Our custom `minival` and `valminusminival` annotations are available for download [here](https://dl.fbaipublicfiles.com/detectron/coco/coco_annotations_minival.tgz).
 Please note that `minival` is exactly equivalent to the recently defined 2017 `val` set.
 Similarly, the union of `valminusminival` and the 2014 `train` is exactly equivalent to the 2017 `train` set. To complete installation of the COCO dataset, you will need to copy the `minival` and `valminusminival` json annotation files to the `coco/annotations` directory referenced above.