open-mmlab · Tau-J · Aug 11, 2023 · Aug 10, 2023 · Aug 10, 2023 · Aug 10, 2023
diff --git a/dataset-index.yml b/dataset-index.yml
@@ -1,71 +1,72 @@
+openxlab: true
 coco2017:
-  dataset: COCO_2017
+  dataset: OpenDataLab/COCO_2017
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_coco2017.sh
 
 mpii:
-  dataset: MPII_Human_Pose
+  dataset: OpenDataLab/MPII_Human_Pose
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_mpii.sh
 
 aic:
-  dataset: AI_Challenger
+  dataset: OpenDataLab/AI_Challenger
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_aic.sh
 
 crowdpose:
-  dataset: CrowdPose
+  dataset: OpenDataLab/CrowdPose
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_crowdpose.sh
 
 halpe:
-  dataset: Halpe
+  dataset: OpenDataLab/Halpe
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_halpe.sh
 
 lapa:
-  dataset: LaPa
+  dataset: OpenDataLab/LaPa
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_lapa.sh
 
 300w:
-  dataset: 300w
+  dataset: OpenDataLab/300w
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_300w.sh
 
 wflw:
-  dataset: WFLW
+  dataset: OpenDataLab/WFLW
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_wflw.sh
 
 onehand10k:
-  dataset: OneHand10K
+  dataset: OpenDataLab/OneHand10K
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_onehand10k.sh
 
 freihand:
-  dataset: FreiHAND
+  dataset: OpenDataLab/FreiHAND
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_freihand.sh
 
 ap10k:
-  dataset: AP-10K
+  dataset: OpenDataLab/AP-10K
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_ap10k.sh
 
 hagrid:
-  dataset: HaGRID
+  dataset: OpenDataLab/HaGRID
   download_root: data
   data_root: data/pose
   script: tools/dataset_converters/scripts/preprocess_hagrid.sh
diff --git a/docs/en/user_guides/prepare_datasets.md b/docs/en/user_guides/prepare_datasets.md
@@ -158,18 +158,18 @@ The heatmap target will be visualized together if it is generated in the pipelin
 
 ## Download dataset via MIM
 
-By using [OpenDataLab](https://opendatalab.com/), you can obtain free formatted datasets in various fields. Through the search function of the platform, you may address the dataset they look for quickly and easily. Using the formatted datasets from the platform, you can efficiently conduct tasks across datasets.
+By using [OpenXLab](https://openxlab.org.cn/datasets), you can obtain free formatted datasets in various fields. Through the search function of the platform, you may address the dataset they look for quickly and easily. Using the formatted datasets from the platform, you can efficiently conduct tasks across datasets.
 
 If you use MIM to download, make sure that the version is greater than v0.3.8. You can use the following command to update, install, login and download the dataset:
 
 ```shell
 # upgrade your MIM
 pip install -U openmim
 
-# install OpenDataLab CLI tools
-pip install -U opendatalab
-# log in OpenDataLab, registry
-odl login
+# install OpenXLab CLI tools
+pip install -U openxlab
+# log in OpenXLab
+openxlab login
 
 # download coco2017 and preprocess by MIM
 mim download mmpose --dataset coco2017

diff --git a/docs/zh_cn/user_guides/prepare_datasets.md b/docs/zh_cn/user_guides/prepare_datasets.md
@@ -158,18 +158,18 @@ python tools/misc/browse_dataset.py configs/body_2d_keypoint/topdown_heatmap/coc
 
 ## 用 MIM 下载数据集
 
-通过使用 [OpenDataLab](https://opendatalab.com/)，您可以直接下载开源数据集。通过平台的搜索功能，您可以快速轻松地找到他们正在寻找的数据集。使用平台上的格式化数据集，您可以高效地跨数据集执行任务。
+通过使用 [OpenXLab](https://openxlab.org.cn/datasets)，您可以直接下载开源数据集。通过平台的搜索功能，您可以快速轻松地找到他们正在寻找的数据集。使用平台上的格式化数据集，您可以高效地跨数据集执行任务。
 
 如果您使用 MIM 下载，请确保版本大于 v0.3.8。您可以使用以下命令进行更新、安装、登录和数据集下载：
 
 ```shell
 # upgrade your MIM
 pip install -U openmim
 
-# install OpenDataLab CLI tools
-pip install -U opendatalab
-# log in OpenDataLab, registry
-odl login
+# install OpenXLab CLI tools
+pip install -U openxlab
+# log in OpenXLab
+openxlab login
 
 # download coco2017 and preprocess by MIM
 mim download mmpose --dataset coco2017

diff --git a/model-index.yml b/model-index.yml
@@ -125,3 +125,5 @@ Import:
 - configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/vipnas_dark_coco-wholebody.yml
 - configs/wholebody_2d_keypoint/topdown_heatmap/coco-wholebody/cspnext_udp_coco-wholebody.yml
 - configs/fashion_2d_keypoint/topdown_heatmap/deepfashion2/res50_deepfasion2.yml
+- configs/fashion_2d_keypoint/topdown_heatmap/deepfashion/hrnet_deepfashion.yml
+- configs/fashion_2d_keypoint/topdown_heatmap/deepfashion/resnet_deepfashion.yml
diff --git a/tools/dataset_converters/scripts/preprocess_300w.sh b/tools/dataset_converters/scripts/preprocess_300w.sh
@@ -3,6 +3,6 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/300w/raw/300w.tar.gz.00 -C $DOWNLOAD_DIR/
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___300w/raw/300w.tar.gz.00 -C $DOWNLOAD_DIR/
 tar -xvf $DOWNLOAD_DIR/300w/300w.tar.00 -C $DATA_ROOT/
-rm -rf $DOWNLOAD_DIR/300w
+rm -rf $DOWNLOAD_DIR/300w $DOWNLOAD_DIR/OpenDataLab___300w
diff --git a/tools/dataset_converters/scripts/preprocess_aic.sh b/tools/dataset_converters/scripts/preprocess_aic.sh
@@ -3,5 +3,5 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/AI_Challenger/raw/AI_Challenger.tar.gz -C $DATA_ROOT
-rm -rf $DOWNLOAD_DIR/AI_Challenger
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___AI_Challenger/raw/AI_Challenger.tar.gz -C $DATA_ROOT
+rm -rf $DOWNLOAD_DIR/OpenDataLab___AI_Challenger
diff --git a/tools/dataset_converters/scripts/preprocess_ap10k.sh b/tools/dataset_converters/scripts/preprocess_ap10k.sh
@@ -3,6 +3,6 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/AP-10K/raw/AP-10K.tar.gz.00 -C $DOWNLOAD_DIR/
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___AP-10K/raw/AP-10K.tar.gz.00 -C $DOWNLOAD_DIR/
 tar -xvf $DOWNLOAD_DIR/AP-10K/AP-10K.tar.00 -C $DATA_ROOT/
-rm -rf $DOWNLOAD_DIR/AP-10K
+rm -rf $DOWNLOAD_DIR/AP-10K $DOWNLOAD_DIR/OpenDataLab___AP-10K
diff --git a/tools/dataset_converters/scripts/preprocess_coco2017.sh b/tools/dataset_converters/scripts/preprocess_coco2017.sh
@@ -3,7 +3,7 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-unzip $DOWNLOAD_DIR/COCO_2017/raw/Images/val2017.zip -d $DATA_ROOT
-unzip $DOWNLOAD_DIR/COCO_2017/raw/Images/train2017.zip -d $DATA_ROOT
-unzip $DOWNLOAD_DIR/COCO_2017/raw/Annotations/annotations_trainval2017.zip -d $DATA_ROOT
-rm -rf $DOWNLOAD_DIR/COCO_2017
+unzip $DOWNLOAD_DIR/OpenDataLab___COCO_2017/raw/Images/val2017.zip -d $DATA_ROOT
+unzip $DOWNLOAD_DIR/OpenDataLab___COCO_2017/raw/Images/train2017.zip -d $DATA_ROOT
+unzip $DOWNLOAD_DIR/OpenDataLab___COCO_2017/raw/Annotations/annotations_trainval2017.zip -d $DATA_ROOT
+rm -rf $DOWNLOAD_DIR/OpenDataLab___COCO_2017
diff --git a/tools/dataset_converters/scripts/preprocess_crowdpose.sh b/tools/dataset_converters/scripts/preprocess_crowdpose.sh
@@ -3,5 +3,5 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/CrowdPose/raw/CrowdPose.tar.gz -C $DATA_ROOT
-rm -rf $DOWNLOAD_DIR/CrowdPose
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___CrowdPose/raw/CrowdPose.tar.gz -C $DATA_ROOT
+rm -rf $DOWNLOAD_DIR/OpenDataLab___CrowdPose
diff --git a/tools/dataset_converters/scripts/preprocess_freihand.sh b/tools/dataset_converters/scripts/preprocess_freihand.sh
@@ -3,5 +3,5 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/FreiHAND/raw/FreiHAND.tar.gz -C $DATA_ROOT
-rm -rf $DOWNLOAD_DIR/FreiHAND
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___FreiHAND/raw/FreiHAND.tar.gz -C $DATA_ROOT
+rm -rf $DOWNLOAD_DIR/OpenDataLab___FreiHAND
diff --git a/tools/dataset_converters/scripts/preprocess_hagrid.sh b/tools/dataset_converters/scripts/preprocess_hagrid.sh
@@ -3,6 +3,6 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-cat $DOWNLOAD_DIR/HaGRID/raw/*.tar.gz.*  | tar -xvz -C $DATA_ROOT/..
+cat $DOWNLOAD_DIR/OpenDataLab___HaGRID/raw/*.tar.gz.*  | tar -xvz -C $DATA_ROOT/..
 tar -xvf $DATA_ROOT/HaGRID.tar -C $DATA_ROOT/..
-rm -rf $DOWNLOAD_DIR/HaGRID
+rm -rf $DOWNLOAD_DIR/OpenDataLab___HaGRID
diff --git a/tools/dataset_converters/scripts/preprocess_halpe.sh b/tools/dataset_converters/scripts/preprocess_halpe.sh
@@ -3,6 +3,6 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/Halpe/raw/Halpe.tar.gz.00 -C $DOWNLOAD_DIR/
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___Halpe/raw/Halpe.tar.gz.00 -C $DOWNLOAD_DIR/
 tar -xvf $DOWNLOAD_DIR/Halpe/Halpe.tar.00 -C $DATA_ROOT/
-rm -rf $DOWNLOAD_DIR/Halpe
+rm -rf $DOWNLOAD_DIR/Halpe $DOWNLOAD_DIR/OpenDataLab___Halpe
diff --git a/tools/dataset_converters/scripts/preprocess_lapa.sh b/tools/dataset_converters/scripts/preprocess_lapa.sh
@@ -3,5 +3,5 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/LaPa/raw/LaPa.tar.gz -C $DATA_ROOT
-rm -rf $DOWNLOAD_DIR/LaPa
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___LaPa/raw/LaPa.tar.gz -C $DATA_ROOT
+rm -rf $DOWNLOAD_DIR/OpenDataLab___LaPa
diff --git a/tools/dataset_converters/scripts/preprocess_mpii.sh b/tools/dataset_converters/scripts/preprocess_mpii.sh
@@ -3,5 +3,5 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/MPII_Human_Pose/raw/MPII_Human_Pose.tar.gz -C $DATA_ROOT
-rm -rf $DOWNLOAD_DIR/MPII_Human_Pose
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___MPII_Human_Pose/raw/MPII_Human_Pose.tar.gz -C $DATA_ROOT
+rm -rf $DOWNLOAD_DIR/OpenDataLab___MPII_Human_Pose
diff --git a/tools/dataset_converters/scripts/preprocess_onehand10k.sh b/tools/dataset_converters/scripts/preprocess_onehand10k.sh
@@ -3,6 +3,6 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/OneHand10K/raw/OneHand10K.tar.gz.00 -C $DOWNLOAD_DIR/
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___OneHand10K/raw/OneHand10K.tar.gz.00 -C $DOWNLOAD_DIR/
 tar -xvf $DOWNLOAD_DIR/OneHand10K/OneHand10K.tar.00 -C $DATA_ROOT/
-rm -rf $DOWNLOAD_DIR/OneHand10K
+rm -rf $DOWNLOAD_DIR/OneHand10K $DOWNLOAD_DIR/OpenDataLab___OneHand10K
diff --git a/tools/dataset_converters/scripts/preprocess_wflw.sh b/tools/dataset_converters/scripts/preprocess_wflw.sh
@@ -3,6 +3,6 @@
 DOWNLOAD_DIR=$1
 DATA_ROOT=$2
 
-tar -zxvf $DOWNLOAD_DIR/WFLW/raw/WFLW.tar.gz.00 -C $DOWNLOAD_DIR/
+tar -zxvf $DOWNLOAD_DIR/OpenDataLab___WFLW/raw/WFLW.tar.gz.00 -C $DOWNLOAD_DIR/
 tar -xvf $DOWNLOAD_DIR/WFLW/WFLW.tar.00 -C $DATA_ROOT/
-rm -rf $DOWNLOAD_DIR/WFLW
+rm -rf $DOWNLOAD_DIR/WFLW $DOWNLOAD_DIR/OpenDataLab___WFLW