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Training Dataset

GiT
|──data
|  |──ade
|  |  |──ADEChallengeData2016
|  |  |  |──annorations
|  |  |  |  |──training & validation
|  |  |  |──images
|  |  |  |  |──training & validation
|  |  |  |──objectInfo150.txt
|  |  |  |──sceneCategories.txt
|  |──coco
|  |  |──annotations
|  |  |  |──*.json
|  |  |──train2017
|  |  |  |──*.jpg
|  |  |──val2017
|  |  |  |──*.jpg
|  |──coco_2014
|  |  |──annotations
|  |  |  |──*.json
|  |  |  |──coco_karpathy_test.json
|  |  |  |──coco_karpathy_train.json
|  |  |  |──coco_karpathy_val_gt.json
|  |  |  |──coco_karpathy_val.json
|  |  |──train2014
|  |  |  |──*.jpg
|  |  |──val2014
|  |  |  |──*.jpg
|  |  |──refcoco
|  |  |  |──*.p
|  |  |──refcoco+
|  |  |  |──*.p
|  |  |──refcocog
|  |  |  |──*.p
|  |──OpenImages
|  |  |──annotations (follow https://github.com/open-mmlab/mmdetection/tree/main/configs/openimages)
|  |  |  |──train-annotations-object-segmentation_sort_resize.csv
|  |  |  |──val-annotations-object-segmentation_sort_resize.csv
|  |  |──OpenImages
|  |  |  |──train
|  |  |  |  |──*.jpg
|  |  |  |──validation
|  |  |  |  |──*.jpg
|  |  |  |──test
|  |  |  |  |──*.jpg
|  |  |──segmentation
|  |  |  |──train
|  |  |  |  |──*.png
|  |  |  |──validation
|  |  |  |  |──*.png
|  |  |  |──test
|  |  |  |  |──*.png

|  |──Objects365
|  |  |──Obj365_v2
|  |  |  |──annotations
|  |  |  |  |──*.jpg
|  |  |  |──train
|  |  |  |  |──patch0
|  |  |  |  |──patch1
             ...
|  |  |  |──val
|  |  |  |  |──patch0
|  |  |  |  |──patch1
             ...
|  |  |  |──annotations
|  |  |  |  |──zhiyuan_objv2_train.json 
|  |  |  |  |──zhiyuan_objv2_val.json
             ...
|  |──lvis_v1
|  |  |──annotations
|  |  |  |──*.json
|  |  |──train2017
|  |  |  |──*.jpg
|  |  |──val2017
|  |  |  |──*.jpg

|  |──coco_stuff164k
|  |  |──annotations
|  |  |  |──train2017
|  |  |  |  |——000000250893_labelTrainIds.png 
|  |  |  |  |——000000250893.png 
|  |  |  |——val2017
|  |  |  |  |——000000229601_labelTrainIds.png
|  |  |  |  |——000000229601.png
|  |  |──images
|  |  |  |──train2017
|  |  |  |  |——*.png 
|  |  |  |——val2017
|  |  |  |  |——*.png 

│   ├── VOCdevkit
│   │   ├── VOC2007
│   │   |   ├── Annotations
│   │   |   |   ├── *.xml
│   │   |   ├── ImageSets
│   │   |   |   ├── Action
│   │   |   |   ├── Layout
│   │   |   |   ├── Main
│   │   |   |   ├── Segmentation
│   │   |   ├── JPEGImages
│   │   |   |   ├── *.jpg
│   │   |   ├── SegmentationClass
│   │   |   |   ├── *.png
│   │   |   ├── SegmentationObject
│   │   |   |   ├── *.png
│   │   ├── VOC2012
│   │   ├── VOC2010
│   │   │   ├── JPEGImages
│   │   │   ├── SegmentationClassContext
│   │   │   ├── ImageSets
│   │   │   │   ├── SegmentationContext
│   │   │   │   │   ├── train.txt
│   │   │   │   │   ├── val.txt
│   │   │   ├── trainval_merged.json

|  |──nuimages
|  |  |──annotations
|  |  |  |── nuimages_v1.0-train.json
|  |  |  |── nuimages_v1.0-val.json
|  |  |──calibrated
|  |  |──samples
|  |  |  |── CAM_BACK
|  |  |  |—— CAM_BACK_LEFT
|  |  |  |—— CAM_BACK_RIGHT
|  |  |  |—— CAM_FRONT
|  |  |  |—— CAM_FRONT_LEFT
|  |  |  |—— CAM_FRONT_RIGHT
|  |  |──v1.0-mini
|  |  |──v1.0-test
|  |  |──v1.0-train
|  |  |──v1.0-val

|  |──nuimages_seg
|  |  |──annotations
|  |  |  |── training
|  |  |  |   |── *.png
|  |  |  |── validation
|  |  |──images
|  |  |  |── training
|  |  |  |   |── *.jpg
|  |  |  |── validation

│   ├── bdd100k
│   │   ├── images
│   │   │   └── 10k
|   │   │   │   ├── test
|   │   │   │   ├── train
|   │   │   │   └── val
│   │   └── labels
│   │   │   └── sem_seg
|   │   │   │   ├── colormaps
|   │   │   │   │   ├──train
|   │   │   │   │   └──val
|   │   │   │   ├── masks
|   │   │   │   │   ├──train
|   │   │   │   │   └──val
|   │   │   │   ├── polygons
|   │   │   │   │   ├──sem_seg_train.json
|   │   │   │   │   └──sem_seg_val.json
|   │   │   │   └── rles
|   │   │   │   │   ├──sem_seg_train.json
|   │   │   │   │   └──sem_seg_val.json

│   ├── mapillary
│   │   ├── training
│   │   │   ├── images
│   │   │   ├── v1.2
|   │   │   │   ├── instances
|   │   │   │   ├── labels
|   │   │   │   └── panoptic
│   │   │   ├── v2.0
|   │   │   │   ├── instances
|   │   │   │   ├── labels
|   │   │   │   ├── panoptic
|   │   │   │   └── polygons
│   │   ├── validation
│   │   │   ├── images
|   │   │   ├── v1.2
|   │   │   │   ├── instances
|   │   │   │   ├── labels
|   │   │   │   └── panoptic
│   │   │   ├── v2.0
|   │   │   │   ├── instances
|   │   │   │   ├── labels
|   │   │   │   ├── panoptic
|   │   │   │   └── polygons

│   ├── ccs
│   │   ├── images
│   │   │   ├── *.jpg
│   │   ├── ccs_synthetic_filtered_mask_12m.json

│   ├── vg
│   │   ├── images
│   │   │   ├── *.jpg
│   │   ├── vg.json

│   ├── refclef
│   │   ├── saiapr_tc-12
│   │   │   ├── 00
│   │   │   |   ├── images
│   │   ├── refs(unc).p
│   │   ├── instances.json

│   ├── cityscapes
│   │   ├── annotations
│   │   ├── leftImg8bit
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── gtFine
│   │   │   ├── train
│   │   │   ├── val

│   ├── kitti
│   │   ├── train
│   │   │   ├── image
│   │   │   ├── *.png
│   │   │   ├── coco
│   │   │   │   ├── kitti_coco_format_train.json
│   │   ├── val
│   │   ├── train.txt
│   │   ├── val.txt

│   ├── crowdhuman
│   │   ├── annotation_train.odgt
│   │   ├── annotation_val.odgt
│   │   ├── train
│   │   │   ├── Images
│   │   │   ├── CrowdHuman_train01.zip
│   │   │   ├── CrowdHuman_train02.zip
│   │   │   ├── CrowdHuman_train03.zip
│   │   ├── val
│   │   │   ├── Images
│   │   │   ├── CrowdHuman_val.zip

│   ├── SUN RGB-D
│   │   ├── images
│   │   │   ├── training
│   │   │   │   ├── *.jpg
│   │   │   ├── validation
│   │   ├── annotations
│   │   │   ├── training
│   │   │   │   ├── *.png
│   │   │   ├── validation

│   ├── nocaps
│   │   ├── annotations
│   │   │   ├── nocaps_test_image_info.json
│   │   │   ├── nocaps_val_4500_captions.json
│   │   ├── images
│   │   │   ├── *.jpg

│   ├── loveDA
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   ├── val
│   │   │   ├── test
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   ├── val

│   ├── potsdam
│   │   ├── img_dir
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── ann_dir
│   │   │   ├── train
│   │   │   ├── val

│   ├── DRIVE
│   │   ├── images
│   │   │   ├── training
│   │   │   ├── validation
│   │   ├── annotations
│   │   │   ├── training
│   │   │   ├── validation

│   ├── WIDERFace
│   │   ├── WIDER_train
│   |   │   ├──0--Parade
│   |   │   ├── ...
│   |   │   ├── Annotations
│   │   ├── WIDER_val
│   |   │   ├──0--Parade
│   |   │   ├── ...
│   |   │   ├── Annotations
│   │   ├── val.txt
│   │   ├── train.txt

│   ├── DeepFashion
│   │   ├── In-shop
|   │   │   ├── Anno
|   │   │   │   ├── segmentation
|   │   │   │   |   ├── DeepFashion_segmentation_train.json
|   │   │   │   |   ├── DeepFashion_segmentation_query.json
|   │   │   │   |   ├── DeepFashion_segmentation_gallery.json
|   │   │   │   ├── list_bbox_inshop.txt
|   │   │   │   ├── list_description_inshop.json
|   │   │   │   ├── list_item_inshop.txt
|   │   │   │   └── list_landmarks_inshop.txt
|   │   │   ├── Eval
|   │   │   │   └── list_eval_partition.txt
|   │   │   ├── Img
|   │   │   │   ├── img
|   │   │   │   │   ├──XXX.jpg
|   │   │   │   ├── img_highres
|   │   │   │   └── ├──XXX.jpg

COCO 2017

python tools/misc/download_dataset.py --dataset-name coco2017

ADE20K

mim download mmsegmentation --dataset ade20k

COCO Caption

python tools/misc/download_dataset.py --dataset-name coco2014

wget https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_train.json
wget https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val.json
wget https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test.json
wget https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_val_gt.json
wget https://storage.googleapis.com/sfr-vision-language-research/datasets/coco_karpathy_test_gt.json

OpenImages

  1. You need to download and extract Open Images dataset.

  2. The Open Images dataset does not have image metas (width and height of the image), which will be used during training and testing (evaluation). We suggest to get test image metas before training/testing by using tools/misc/get_image_metas.py.

    Usage

    python tools/misc/get_image_metas.py ${CONFIG} \
--dataset ${DATASET TYPE} \  # train or val or test
--out ${OUTPUT FILE NAME}

  3. The directory should be like this:

    mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── OpenImages
│   │   ├── annotations
│   │   │   ├── bbox_labels_600_hierarchy.json
│   │   │   ├── class-descriptions-boxable.csv
│   │   │   ├── oidv6-train-annotations-bbox.scv
│   │   │   ├── validation-annotations-bbox.csv
│   │   │   ├── validation-annotations-human-imagelabels-boxable.csv
│   │   │   ├── validation-image-metas.pkl      # get from script
│   │   ├── challenge2019
│   │   │   ├── challenge-2019-train-detection-bbox.txt
│   │   │   ├── challenge-2019-validation-detection-bbox.txt
│   │   │   ├── class_label_tree.np
│   │   │   ├── class_sample_train.pkl
│   │   │   ├── challenge-2019-validation-detection-human-imagelabels.csv       # download from official website
│   │   │   ├── challenge-2019-validation-metas.pkl     # get from script
│   │   ├── OpenImages
│   │   │   ├── train           # training images
│   │   │   ├── test            # testing images
│   │   │   ├── validation      # validation images

Object365

  1. You need to download and extract Objects365 dataset. Users can download Objects365 V2 by using tools/misc/download_dataset.py.

    Usage

    python tools/misc/download_dataset.py --dataset-name objects365v2 \
--save-dir ${SAVING PATH} \
--unzip \
--delete  # Optional, delete the download zip file

    Note: There is no download link for Objects365 V1 right now. If you would like to download Objects365-V1, please visit official website to concat the author.

  2. The directory should be like this:

    mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── Objects365
│   │   ├── Obj365_v1
│   │   │   ├── annotations
│   │   │   │   ├── objects365_train.json
│   │   │   │   ├── objects365_val.json
│   │   │   ├── train        # training images
│   │   │   ├── val          # validation images
│   │   ├── Obj365_v2
│   │   │   ├── annotations
│   │   │   │   ├── zhiyuan_objv2_train.json
│   │   │   │   ├── zhiyuan_objv2_val.json
│   │   │   ├── train        # training images
│   │   │   │   ├── patch0
│   │   │   │   ├── patch1
│   │   │   │   ├── ...
│   │   │   ├── val          # validation images
│   │   │   │   ├── patch0
│   │   │   │   ├── patch1
│   │   │   │   ├── ...

LVIS 1.0

wget https://dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip
wget https://dl.fbaipublicfiles.com/LVIS/lvis_v1_val.json.zip

COCO Stuff 164k

For COCO Stuff 164k dataset, please run the following commands to download and convert the augmented dataset.

# download
mkdir coco_stuff164k && cd coco_stuff164k
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip
wget http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip

# unzip
unzip train2017.zip -d images/
unzip val2017.zip -d images/
unzip stuffthingmaps_trainval2017.zip -d annotations/

# --nproc means 8 process for conversion, which could be omitted as well.
python tools/dataset_converters/coco_stuff164k.py /path/to/coco_stuff164k --nproc 8

By convention, mask labels in /path/to/coco_stuff164k/annotations/*2017/*_labelTrainIds.png are used for COCO Stuff 164k training and testing.

The details of this dataset could be found at here.

VOC0712

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
tar -xvf VOCtrainval_06-Nov-2007.tar 
wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar -xvf VOCtrainval_11-May-2012.tar

nuImages

Download samples and metadata from https://www.nuscenes.org/nuimages#download tar -zxvf nuimages-v1.0-all-samples.tgz

tar -zxvf nuimages-v1.0-all-metadata.tgz

python -u tools/dataset_converters/nuimage_converter.py --data-root ${DATA_ROOT} --version ${VERSIONS} \
                                                    --out-dir ${OUT_DIR} --nproc ${NUM_WORKERS} --extra-tag ${TAG}
  • --data-root: the root of the dataset, defaults to ./data/nuimages.
  • --version: the version of the dataset, defaults to v1.0-mini. To get the full dataset, please use --version v1.0-train v1.0-val v1.0-mini
  • --out-dir: the output directory of annotations and semantic masks, defaults to ./data/nuimages/annotations/.
  • --nproc: number of workers for data preparation, defaults to 4. Larger number could reduce the preparation time as images are processed in parallel.
  • --extra-tag: extra tag of the annotations, defaults to nuimages. This can be used to separate different annotations processed in different time for study.
python -u tools/dataset_converters/nuimage_converter.py --data-root ./data/nuimages --version v1.0-train v1.0-val v1.0-mini \
                                                    --out-dir ./data/nuimages/annotations/ --nproc 8

nuImages Segmentation

python tools/dataset_preprocess/link_nuimage_seg.py

OpenImages Instance Segmentation

cd data/OpenImages
mkdir segmentation
cd segmentation 
mkdir train
mkdir validation
mkdir test
python tools/dataset_preprocess/download_open_inseg.py

sort instances by ImageID:

python tools/dataset_preprocess/sort_instances.py

this command will create train-annotations-object-segmentation_sort_resize.csv and validation-annotations-object-segmentation_sort_resize.csv in data/OpenImages/annotations.

Pascal Context

The training and validation set of Pascal Context could be download from here. You may also download test set from here after registration.

To split the training and validation set from original dataset, you may download trainval_merged.json from here.

If you would like to use Pascal Context dataset, please install Detail and then run the following command to convert annotations into proper format.

python tools/dataset_converters/pascal_context.py data/VOCdevkit data/VOCdevkit/VOC2010/trainval_merged.json

BDD100K

  • You could download BDD100k datasets from here after registration.

  • Or download from OpenDataLab

  • You can download images and masks by clicking 10K Images button and Segmentation button.

  • After download, unzip by the following instructions:

    unzip ~/bdd100k_images_10k.zip -d ~/mmsegmentation/data/
unzip ~/bdd100k_sem_seg_labels_trainval.zip -d ~/mmsegmentation/data/

  • And get

mmsegmentation
├── mmseg
├── tools
├── configs
├── data
│   ├── bdd100k
│   │   ├── images
│   │   │   └── 10k
|   │   │   │   ├── test
|   │   │   │   ├── train
|   │   │   │   └── val
│   │   └── labels
│   │   │   └── sem_seg
|   │   │   │   ├── colormaps
|   │   │   │   │   ├──train
|   │   │   │   │   └──val
|   │   │   │   ├── masks
|   │   │   │   │   ├──train
|   │   │   │   │   └──val
|   │   │   │   ├── polygons
|   │   │   │   │   ├──sem_seg_train.json
|   │   │   │   │   └──sem_seg_val.json
|   │   │   │   └── rles
|   │   │   │   │   ├──sem_seg_train.json
|   │   │   │   │   └──sem_seg_val.json

## Mapillary Vistas Datasets

- The dataset could be download [here](https://www.mapillary.com/dataset/vistas) after registration.

- Mapillary Vistas Dataset use 8-bit with color-palette to store labels. No conversion operation is required.

- Assumption you have put the dataset zip file in `mmsegmentation/data/mapillary`

- Please run the following commands to unzip dataset.

  ```bash
  cd data/mapillary
  unzip An-ZjB1Zm61yAZG0ozTymz8I8NqI4x0MrYrh26dq7kPgfu8vf9ImrdaOAVOFYbJ2pNAgUnVGBmbue9lTgdBOb5BbKXIpFs0fpYWqACbrQDChAA2fdX0zS9PcHu7fY8c-FOvyBVxPNYNFQuM.zip
  • After unzip, you will get Mapillary Vistas Dataset like this structure. Semantic segmentation mask labels in labels folder.

CCS(CC3M+CC12M+SBU)

wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/datasets/ccs_synthetic_filtered.json

remove failed images:

python tools/dataset_preprocess/mask_ccs.py

Visual Genome

openxlab dataset get --dataset-repo OpenDataLab/Visual_Genome_Dataset_V1_dot_2
wget https://storage.googleapis.com/sfr-vision-language-research/datasets/vg_caption.json
python tools/dataset_preprocess/vg_caption.py

refcoco, refcoco+,refcocog

wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco.zip
wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcoco+.zip
wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refcocog.zip

  • use images from coco 2014

  • use refs(unc).p for refcoco+ and refcoco

  • use refs(umd).p for refcocog

refclef

wget https://bvisionweb1.cs.unc.edu/licheng/referit/data/refclef.zip

download image data from https://www.imageclef.org/SIAPRdata

Flickr30K

reference: https://github.com/bianhao123/TransVG

Zero-Shot Dataset

Cityscapes Segmentation

The data could be found here after registration.

By convention, **labelTrainIds.png are used for cityscapes training. We provided a script based on cityscapesscriptsto generate **labelTrainIds.png.

# --nproc means 8 process for conversion, which could be omitted as well.
python tools/dataset_converters/cityscapes_seg.py data/cityscapes --nproc 8

Cityscapes Detection and Instance Segmentation

The cityscapes annotations have to be converted into the coco format using tools/dataset_converters/cityscapes.py:

pip install cityscapesscripts
python tools/dataset_converters/cityscapes.py ./data/cityscapes --nproc 8 --out-dir ./data/cityscapes/annotations

KITTI

wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_image_2.zip
wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_object_label_2.zip

download train.txt, val.txt, test.txt from https://github.com/open-mmlab/OpenPCDet/blob/master/data/kitti/ImageSets/train.txt convert kitti to coco format:

python tools/dataset_preprocess/kitti2coco.py

reference: https://github.com/mingkyun/kitti_mmdetection

SUN RGB-D

reference: https://github.com/Barchid/RGBD-Seg

download sunrgbd:

python tools/dataset_preprocess/prepare_sunrgbd.py

convert sunrgbd to mmsegmentation format:

python tools/dataset_preprocess/organize_sunrgbd.py

NoCaps

mkdir nocaps && cd nocaps
ln -s /u/sshi/workspace_ptmp/workspace_hy/dataset/OpenImages/OpenImages/validation ./images
mkdir annotations && annotations
wget https://nocaps.s3.amazonaws.com/nocaps_val_4500_captions.json
wget https://s3.amazonaws.com/nocaps/nocaps_test_image_info.json

CrowdHuman

download data from https://www.crowdhuman.org/

convert to coco:

python ./tools/dataset_converters/crowdhuman2coco.py -i ./data/crowdhuman -o ./data/crowdhuman/annotations

Few-Shot Dataset

DRIVE

The training and validation set of DRIVE could be download from kaggle.

To convert DRIVE dataset to MMSegmentation format, you should run the following command:

python tools/dataset_converters/drive.py /path/to/DRIVE

The script will make directory structure automatically.

LoveDA

The data could be downloaded from Google Drive here.

Or it can be downloaded from zenodo, you should run the following command:

# Download Train.zip
wget https://zenodo.org/record/5706578/files/Train.zip
# Download Val.zip
wget https://zenodo.org/record/5706578/files/Val.zip
# Download Test.zip
wget https://zenodo.org/record/5706578/files/Test.zip

For LoveDA dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/loveda.py /path/to/loveDA

Using trained model to predict test set of LoveDA and submit it to server can be found here.

More details about LoveDA can be found here.

Potsdam

The Potsdam dataset is for urban semantic segmentation used in the 2D Semantic Labeling Contest - Potsdam.

The dataset can be requested at the challenge homepage. Or download on BaiduNetdisk，password：mseg, Google Drive and OpenDataLab. The '2_Ortho_RGB.zip' and '5_Labels_all_noBoundary.zip' are required. Create a folder containing these two zip files.

For Potsdam dataset, please run the following command to re-organize the dataset.

python tools/dataset_converters/potsdam.py /path/to/potsdam

In our default setting, it will generate 3456 images for training and 2016 images for validation.

WIDERFace

To use the WIDER Face dataset you need to download it and extract to the data/WIDERFace folder. Annotation in the VOC format can be found in this repo. You should move the annotation files from WIDER_train_annotations and WIDER_val_annotations folders to the Annotation folders inside the corresponding directories WIDER_train and WIDER_val. Also annotation lists val.txt and train.txt should be copied to data/WIDERFace from WIDER_train_annotations and WIDER_val_annotations.

DeepFashion

MMFashion develops "fashion parsing and segmentation" module based on the dataset DeepFashion-Inshop. Its annotation follows COCO style. To use it, you need to first download the data. Note that we only use "img_highres" in this task. The file tree should be like this:

mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── DeepFashion
│   │   ├── In-shop
|   │   │   ├── Anno
|   │   │   │   ├── segmentation
|   │   │   │   |   ├── DeepFashion_segmentation_train.json
|   │   │   │   |   ├── DeepFashion_segmentation_query.json
|   │   │   │   |   ├── DeepFashion_segmentation_gallery.json
|   │   │   │   ├── list_bbox_inshop.txt
|   │   │   │   ├── list_description_inshop.json
|   │   │   │   ├── list_item_inshop.txt
|   │   │   │   └── list_landmarks_inshop.txt
|   │   │   ├── Eval
|   │   │   │   └── list_eval_partition.txt
|   │   │   ├── Img
|   │   │   │   ├── img
|   │   │   │   │   ├──XXX.jpg
|   │   │   │   ├── img_highres
|   │   │   │   └── ├──XXX.jpg