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Datasets

The Hailo Model Zoo works with TFRecord files which store the images and labels of the dataset for evaluation and calibration.
The instructions on how to create the TFRecord files are given below. By default, datasets are stored in the following path:
~/.hailomz

We recommend to define the data directory path yourself, by setting the HMZ_DATA environment variable.

export HMZ_DATA=/new/path/for/storage/

ImageNet

To evaluate/optimize/compile the classification models of the Hailo Model Zoo you should generate the ImageNet TFRecord files (manual download is required).

  1. Download the ImageNet dataset from here. The expected dataset structure:
    imagenet
|_ train
|  |_ n01440764
|  |_ ...
|  |_ n15075141
|_ val
|  |_ n01440764
|  |_ ...
|  |_ n15075141
|_ ...
    * To avoid downloading the ImageNet training data, you may consider using the validation dataset for calibration (does not apply for finetune).

  2. Run the create TFRecord scripts:

    python hailo_model_zoo/datasets/create_imagenet_tfrecord.py val --img /path/to/imagenet/val/
python hailo_model_zoo/datasets/create_imagenet_tfrecord.py calib --img /path/to/imagenet/train/

COCO2017

To evaluate/optimize/compile the object detection / pose estimation models of the Hailo Model Zoo you should generate the COCO (link) TFRecord files.
Run the create TFRecord scripts to download the dataset and generate the TFRecord files:
python hailo_model_zoo/datasets/create_coco_tfrecord.py val2017
python hailo_model_zoo/datasets/create_coco_tfrecord.py calib2017
To evaluate/optimize/compile the single person pose estimation models of the Hailo Model Zoo you should generate the single-person COCO TFRecord files.
Run the create TFRecord scripts to download the dataset and generate the TFRecord files:
python hailo_model_zoo/datasets/create_coco_single_person_tfrecord.py val2017
python hailo_model_zoo/datasets/create_coco_single_person_tfrecord.py calib2017

Manual Download (Optional)

  1. Download COCO (here). The expected dataset structure:

    Annotations:

    annotations
|_ captions_train2017.json
|_ captions_val2017.json
|_ instances_train2017.json
|_ instances_val2017.json
|_ person_keypoints_train2017.json
|_ person_keypoints_val2017.json

    Validation set:

    val2017
|_ 000000000139.jpg
|_ 000000000285.jpg
|_ 000000000632.jpg
|_ 000000000724.jpg
|_ 000000000776.jpg
|_ 000000000785.jpg
|_ 000000000802.jpg
|_ 000000000872.jpg
|_ 000000000885.jpg
|_ ...

    Training set:

    train2017
|_ 000000000009.jpg
|_ 000000000025.jpg
|_ 000000000030.jpg
|_ 000000000034.jpg
|_ 000000000036.jpg
|_ 000000000042.jpg
|_ 000000000049.jpg
|_ 000000000061.jpg
|_ 000000000064.jpg
|_ ...

  2. Run the creation scripts:

    python hailo_model_zoo/datasets/create_coco_tfrecord.py val2017 --img /path/to/val2017 --det /path/to/annotations
python hailo_model_zoo/datasets/create_coco_tfrecord.py calib2017 --img /path/to/train2017 --det /path/to/annotations

Cityscapes

To evaluate/optimize/compile the semantic segmentation models of the Hailo Model Zoo you should generate the Cityscapes TFRecord files (manual download is required).

  1. Download the Cityscapes dataset from here. The expected dataset structure:

    Cityscapes
|_ gtFine
|  |_ train
|  |_ test
|  |_ val
|_ leftImg8bit
|  |_ train
|  |_ test
|  |_ val
|  |_ train_extra
|_ ...

  2. Run the create TFRecord scripts:

    python hailo_model_zoo/datasets/create_cityscapes_tfrecord.py val --data /path/to/Cityscapes/
python hailo_model_zoo/datasets/create_cityscapes_tfrecord.py calib --data /path/to/Cityscapes/

WIDERFACE

To evaluate/optimize/compile the face detection models of the Hailo Model Zoo you should generate the WIDERFACE (link) TFRecord files.
Run the create TFRecord scripts to download the dataset and generate the TFRecord files:
python hailo_model_zoo/datasets/create_widerface_tfrecord.py calib
python hailo_model_zoo/datasets/create_widerface_tfrecord.py val

Manual Download (Optional)

  1. Download the following from here:

    • WIDER Face Training Images
    • WIDER Face Validation Images
    • Face annotations

  2. Download the following from here

    Expected directory structure:

    widerface/
|_ wider_face_split
|  |_ readme.txt
|  |_ wider_face_test_filelist.txt
|  |_ wider_face_test.mat
|  |_ wider_face_train_bbx_gt.txt
|  |_ wider_face_train.mat
|  |_ wider_face_val_bbx_gt.txt
|  |_ wider_face_val.mat
|  |_ wider_hard_val.mat
|_ WIDER_train
|  |_ images
|     |_ 0--Parade
|     |_ 10--People_Marching
|     |_ 11--Meeting
|     |_ ...
|_ WIDER_val
   |_ images
      |_ 0--Parade
      |_ 10--People_Marching
      |_ 11--Meeting
      |_ ...

  3. Run the creation scripts

    python hailo_model_zoo/datasets/create_widerface_tfrecord.py calib --img /path/to/widerface --gt_mat_path /path/to/wider_face_split --hard_mat_path /path/to/wider_face_split
python hailo_model_zoo/datasets/create_widerface_tfrecord.py val --img /path/to/widerface --gt_mat_path /path/to/wider_face_split --hard_mat_path /path/to/wider_face_split

VisDrone

To evaluate/optimize/compile the visdrone object detection models of the Hailo Model Zoo you should generate the VisDrone (link) TFRecord files.
Run the create TFRecord scripts to download the dataset and generate the TFRecord files:
python hailo_model_zoo/datasets/create_visdrone_tfrecord.py train
python hailo_model_zoo/datasets/create_visdrone_tfrecord.py val

Manual Download (Optional)

  1. Download VisDrone (here). The expected dataset structure:

    Training set:

    VisDrone2019-DET-train/
|_ annotations
|  |_ 0000002_00005_d_0000014.txt
|  |_ 0000002_00448_d_0000015.txt
|  |_ ...
|_ images
   |_ 0000002_00005_d_0000014.jpg
   |_ 0000002_00448_d_0000015.jpg
   |_ ...

    Validation set:

    VisDrone2019-DET-val/
|_ annotations
|  |_ 0000001_02999_d_0000005.txt
|  |_ 0000001_03499_d_0000006.txt
|  |_ ...
|_ images
   |_ 0000001_02999_d_0000005.jpg
   |_ 0000001_03499_d_0000006.jpg
   |_ ...

  2. Run the creation scripts:

    python hailo_model_zoo/datasets/create_visdrone_tfrecord.py train -d /path/to/VisDrone2019-DET-train
python hailo_model_zoo/datasets/create_visdrone_tfrecord.py val -d /path/to/VisDrone2019-DET-val

Pascal VOC augmented dataset

Run the creation scripts:

python hailo_model_zoo/datasets/create_pascal_tfrecord.py calib
python hailo_model_zoo/datasets/create_pascal_tfrecord.py val

Manual Download (Optional)

  1. Download the dataset from here. Expected dataset structure:

    benchmark_RELEASE
|_ dataset
 |_ cls
 |_ img
 |_ inst
 |_ train.txt
 |_ val.txt

  2. run the creation scripts:

    python hailo_model_zoo/datasets/create_pascal_tfrecord.py calib --root benchmark_RELEASE/dataset
python hailo_model_zoo/datasets/create_pascal_tfrecord.py val --root benchmark_RELEASE/dataset

D2S augmented dataset

Run the creation scripts:

python hailo_model_zoo/datasets/create_d2s_tfrecord.py calib
python hailo_model_zoo/datasets/create_d2s_tfrecord.py val

Manual Download (Optional)

  1. Download the dataset from here. Extract using 'tar -xf d2s_images_v1.1.tar.xz'. Expected dataset structure:

    |_ images
 |_ D2S_000200.jpg
 |_ D2S_000201.jpg
 |_ ...

  2. Download the annotations from here. Extract using 'tar -xf d2s_annotations_v1.1.tar.xz'. Expected annotations structure:

    |_ annotations
 |_ D2S_augmented.json
 |_ D2S_validation.json
 |_ ...

  3. run the creation scripts:

    python hailo_model_zoo/datasets/create_d2s_tfrecord.py calib --img /path/to/dataset --det /path/to/annotations/D2S_augmented.json
python hailo_model_zoo/datasets/create_d2s_tfrecord.py val --img /path/to/dataset --det /path/to/annotations/D2S_validation.json

NYU Depth V2

Run the creation scripts:

python hailo_model_zoo/datasets/create_nyu_depth_v2_tfrecord.py calib
python hailo_model_zoo/datasets/create_nyu_depth_v2_tfrecord.py val

Manual Download (Optional)

  1. Download the dataset from here. Extract using 'tar -xf nyudepthv2.tar.gz'. Expected dataset structure:

    |_ train
 |_ study_0300
     |_ 00626.h5
     |_ 00631.h5
     |_ ...
 |_ ...
|_ val
 |_ official
     |_ 00001.h5
     |_ 00002.h5
     |_ 00009.h5
     |_ 00014.h5
     |_ ...

  2. run the creation scripts:

    python hailo_model_zoo/datasets/create_nyu_depth_v2_tfrecord.py calib --data ./nyu_depth_v2/
python hailo_model_zoo/datasets/create_nyu_depth_v2_tfrecord.py val --data ./nyu_depth_v2/

AFLW2k3d and 300W-LP

Run the creation scripts:

python hailo_model_zoo/datasets/create_300w-lp_tddfa_tfrecord.py
python hailo_model_zoo/datasets/create_aflw2k3d_tddfa_tfrecord.py

Manual Download (Optional)

  1. Download the augmented_cropped 300W-LP dataset from here and extract. Expected structure:

    train_aug_120x120
|_ AFW_AFW_1051618982_1_0_10.jpg
|_ AFW_AFW_1051618982_1_0_11.jpg
|_ AFW_AFW_1051618982_1_0_12.jpg
|_ AFW_AFW_1051618982_1_0_13.jpg
|_ AFW_AFW_1051618982_1_0_1.jpg
|_ AFW_AFW_1051618982_1_0_2.jpg
|_ AFW_AFW_1051618982_1_0_3.jpg
|_ AFW_AFW_1051618982_1_0_4.jpg
|_ ...

  2. Run

    python hailo_model_zoo/datasets/create_300w-lp_tddfa_tfrecord.py --dir /path/to/train_aug_120x120

  3. Download the following files:

    • the official dataset from here
    • the cropped dataset from here
    • The following files from here
      • AFLW2000-3D.pose.npy
      • AFLW2000-3D.pts68.npy
      • AFLW2000-3D-Reannotated.pts68.npy
      • AFLW2000-3D_crop.roi_box.npy

    The expected structure:

    aflw2k3d_tddfa
|_ AFLW2000-3D_crop.roi_box.npy
|_ AFLW2000-3D.pose.npy
|_ AFLW2000-3D.pts68.npy
|_ AFLW2000-3D-Reannotated.pts68.npy
|_ test.data
   |_ AFLW2000
   |   |_ Code
   |   |   |_ Mex
   |   |   |_ ModelGeneration
   |   |_ image00002.jpg
   |   |_ image00002.mat
   |   |_ image00004.jpg
   |   |_ image00004.mat
   |   |_ ...
   |_ AFLW2000-3D_crop
   |   |_ image00002.jpg
   |   |_ image00004.jpg
   |   |_ image00006.jpg
   |   |_ image00008.jpg
   |   |_ ...
   |_ AFLW2000-3D_crop.list
   |_ AFLW_GT_crop
   |   |_ ...
   |_ AFLW_GT_crop.list

  4. Run the following:

    python hailo_model_zoo/datasets/create_aflw2k3d_tddfa_tfrecord.py --dir /path/to/aflw2k3d_tddfa

Hand Landmark

Run the creation script:

python hailo_model_zoo/datasets/create_hand_landmark_tfrecord.py

Manual Download (Optional)

  1. Download the dataset from here and extract. Expected structure:

    Hands               00  000
|_ Hand_0011695.jpg
|_ Hand_0011696.jpg
|_ Hand_0011697.jpg
|_ ...

  2. Run

    python hailo_model_zoo/datasets/create_hand_landmark_tfrecord.py --img /path/to/Hands

Market1501

Run the creation scripts:

python hailo_model_zoo/datasets/create_market_tfrecord.py val
python hailo_model_zoo/datasets/create_market_tfrecord.py calib

Manual Download (Optional)

  1. Download the dataset from here and extract.
    Expected structure:
    Market-1501-v15.09.15
|_ bounding_box_test
 |_ 0000_c1s1_000151_01.jpg
 |_ 0000_c1s1_000376_03.jpg
 |_ ...
|_ bounding_box_train
 |_ 0002_c1s1_000451_03.jpg
 |_ 0002_c1s1_000551_01.jpg
 |_ ...
|_ gt_bbox
 |_ 0001_c1s1_001051_00.jpg
 |_ 0001_c1s1_002301_00.jpg
 |_ ...
|_ gt_query
 |_ 0001_c1s1_001051_00_good.mat
 |_ 0001_c1s1_001051_00_junk.mat
 |_ ...
|_ query
 |_ 0001_c1s1_001051_00.jpg
 |_ 0001_c2s1_000301_00.jpg
 |_ ...

  2. Run

    python hailo_model_zoo/datasets/create_market_tfrecord.py val --img path/to/Market-1501-v15.09.15/
python hailo_model_zoo/datasets/create_market_tfrecord.py calib --img path/to/Market-1501-v15.09.15/bounding_box_train/

PETA

To evaluate/optimize/compile the person attribute models of the Hailo Model Zoo you should generate the PETA TFRecord files (manual download is required).

  1. Download the PETA dataset from here. The expected dataset structure:

    PETA
|_ images
|  |_ 00001.png
|  |_ ...
|  |_ 19000.png
|_ PETA.mat

  2. Run the create TFRecord scripts:

    python hailo_model_zoo/datasets/create_peta_tfrecord.py test --data /path/to/PETA/
python hailo_model_zoo/datasets/create_peta_tfrecord.py train --data /path/to/PETA/

CelebA

To evaluate/optimize/compile the face attribute models of the Hailo Model Zoo you should generate the CelebA TFRecord files (manual download is required).

  1. Download the CelebA dataset from here. The expected dataset structure:

    Celeba
|_ img_align_celeba_png
|  |_ 000001.jpg
|  |_ ...
|  |_ 202599.jpg
|_ list_attr_celeba.txt
|_ list_eval_partition.txt

  2. Run the create TFRecord scripts:

    python hailo_model_zoo/datasets/create_celeba_tfrecord.py val --data /path/to/CelebA/
python hailo_model_zoo/datasets/create_celeba_tfrecord.py train --data /path/to/CelebA/

LFW

To evaluate/optimize/compile the face recognition models of the Hailo Model Zoo you should generate the arcface_lfw TFRecord files

Run the creation scripts:

python hailo_model_zoo/datasets/create_arcface_lfw_tfrecord.py calib
python hailo_model_zoo/datasets/create_arcface_lfw_tfrecord.py val

Manual Download (Optional)

  1. Download LFW dataset from here

  2. Download LFW pairs file from here

  3. Run the scripts:

    python hailo_model_zoo/datasets/create_arcface_lfw_tfrecord.py calib --tgz /path/to/lfw.tgz --pairs /path/to/pairs.txt
python hailo_model_zoo/datasets/create_arcface_lfw_tfrecord.py val --tgz /path/to/lfw.tgz --pairs /path/to/pairs.txt

BSD100

To evaluate/optimize/compile the super resolution models of the Hailo Model Zoo you should generate the BSD100 TFRecord files.

Run the creation scripts:

python hailo_model_zoo/datasets/create_bsd100_tfrecord.py val
python hailo_model_zoo/datasets/create_bsd100_tfrecord.py calib

Manual Download (Optional)

  1. Download the BSD100 dataset from here and extract. The expected dataset structure:

    BSD100
|_ GTmod12
|  |_ 101085.png
|  |_ ...
|  |_ 97033.png
|_ GTmod16
|  |_ ...
|_ LRbicx8
|  |_ ...
|_ LRbicx4
|  |_ ...
|_ LRbicx3
|  |_ ...
|_ LRbicx2
|  |_ ...
|_ LRbicx16
|  |_ ...
|_ original
|  |_ ...

  2. Run the scripts:

    python hailo_model_zoo/datasets/create_bsd100_tfrecord.py val --lr /path/to/LRbicx4 --hr /path/to/GTmod12
python hailo_model_zoo/datasets/create_bsd100_tfrecord.py calib --lr /path/to/LRbicx4 --hr /path/to/GTmod12

CLIP_CIFAR100

To evaluate/optimize/compile the CLIP models of the Hailo Model Zoo you should generate the CIFAR100 TFRecord files.

Run the creation scripts:

python hailo_model_zoo/datasets/create_clip_cifar100_tfrecord.py val
python hailo_model_zoo/datasets/create_clip_cifar100_tfrecord.py calib

LOL

To evaluate/optimize/compile the low light enhancement models of the Hailo Model Zoo you should generate the LOL TFRecord files.

Run the creation scripts:

python hailo_model_zoo/datasets/create_lol_tfrecord.py val
python hailo_model_zoo/datasets/create_lol_tfrecord.py calib

Manual Download (Optional)

  1. Download the LOL dataset from here and extract. The expected dataset structure:

    lol_dataset
|_ eval15
   |_ high
   |   |_ 111.png
   |   |_ 146.png
   |   |_ ...
   |_ low
   |   |_ 111.png
   |   |_ 146.png
   |   |_ ...
|_ our485
   |_ high
   |   |_ 100.png
   |   |_ 101.png
   |   |_ ...
   |_ low
   |   |_ 100.png
   |   |_ 101.png
   |   |_ ...

  2. Run the scripts:

    python hailo_model_zoo/datasets/create_lol_tfrecord.py val --ll /path/to/val/lowlight/images --lle /path/to/val/highlight/images
python hailo_model_zoo/datasets/create_lol_tfrecord.py calib --ll /path/to/train/lowlight/images --lle /path/to/train/highlight/images

BSD68

To evaluate/optimize/compile the image denoising models of the Hailo Model Zoo you should generate the BSD68 TFRecord files.

Run the creation scripts:

python hailo_model_zoo/datasets/create_bsd68_tfrecord.py val
python hailo_model_zoo/datasets/create_bsd68_tfrecord.py calib

Manual Download (Optional)

  1. Download the BSD100 dataset from here and extract. The expected dataset structure:

    test
|_ BSD68
|  |_ test001.png
|  |_ ...
|  |_ test068.png
|_ CBSD68
|  |_ ...
|_ Kodak
|  |_ ...
|_ McMaster
|  |_ ...
|_ Set12
|  |_ ...
|_ Urban100
|  |_ ...
|_ LRbicx16

  2. Run the scripts:

    python hailo_model_zoo/datasets/create_bsd100_tfrecord.py BSD68 val --data-path <BSD68-extracted-data-folder>
python hailo_model_zoo/datasets/create_bsd100_tfrecord.py BSD68 calib --data-path <BSD68-extracted-data-folder>

CBSD68

To evaluate/optimize/compile the image denoising models of the Hailo Model Zoo you should generate the CBSD68 TFRecord files.

Run the creation scripts:

python hailo_model_zoo/datasets/create_bsd68_tfrecord.py CBSD68 val
python hailo_model_zoo/datasets/create_bsd68_tfrecord.py CBSD68 calib

Manual Download (Optional)

  1. Download the BSD100 dataset from here and extract. The expected dataset structure:

    test
|_ BSD68
|  |_ ...
|_ CBSD68
|  |_ test001.png
|  |_ ...
|  |_ test068.png
|_ Kodak
|  |_ ...
|_ McMaster
|  |_ ...
|_ Set12
|  |_ ...
|_ Urban100
|  |_ ...
|_ LRbicx16

  2. Run the scripts:

    python hailo_model_zoo/datasets/create_bsd100_tfrecord.py CBSD68 val --data-path <CBSD68-extracted-data-folder>
python hailo_model_zoo/datasets/create_bsd100_tfrecord.py CBSD68 calib --data-path <CBSD68-extracted-data-folder>

KITTI_STEREO

To evaluate/optimize/compile the stereo models of the Hailo Model Zoo you should generate the KITTI Stereo TFRecord files.

Manual Download

  1. Download the KITTI Stereo dataset from here. One must request access and await approval.

  2. Extract the dataset. The expected dataset structure:

    kitti_stereo
|_ testing
   |_ image_2
   |   |_ 000000_10.png
   |   |_ 000000_11.png
   |   |_ ...
   |_ image_3
   |   |_ 000000_10.png
   |   |_ 000000_11.png
   |   |_ ...
|_ training
   |_ image_2
   |   |_ 000000_10.png
   |   |_ 000000_11.png
   |   |_ ...
   |_ disp_occ_0
   |   |_ 000000_10.png
   |   |_ 000001_10.png
   |   |_ 000002_10.png
   |   |_ ...

  3. Run the scripts:

    python hailo_model_zoo/datasets/create_kitti_stereo_tfrecord.py calib --data <TRAIN_DIR>
python hailo_model_zoo/datasets/create_kitti_stereo_tfrecord.py val --data <VALIDATION_DIR>

KINETICS400

To evaluate/optimize/compile the video classification models of the Hailo Model Zoo you should generate the KINETICS400 TFRecord files.

Manual Download

  1. Download the kinetics400 dataset from here. Follow the instructions to download the dataset.

  2. The expected dataset structure: .. code-block:

    k400/videos
|_ test
   |_ abseiling
   |   |_ 0aSqlZT8QmM_000048_000058.mp4
   |   |_ 0xreS8KFbrw_000417_000427.mp4
   |   |_ ...
   |_ air drumming
   |   |_ 013SMb0SX8I_000020_000030.mp4
   |   |_ 013SMb0SX8I_000020_000030.mp4
   |   |_ ...
|_ train
   |_ abseiling
   |   |_ 0347ZoDXyP0_000095_000105.mp4
   |   |_ 035LtPeUFTE_000085_000095.mp4
   |   |_ ...
   |_ air drumming
   |   |_ 03V2idM7_KY_000003_000013.mp4
   |   |_ 1R7Ds_000003_000013.mp4
   |   |_ 0c1bhfxioqE_000078_000088.mp4
   |   |_ ...
|_ val
   |_ abseiling
   |   |_ 0wR5jVB-WPk_000417_000427.mp4
   |   |_ 3caPS4FHFF8_000036_000046.mp4
   |   |_ ...
   |_ air drumming
   |   |_ 2cPLjY5AWXU_000001_000011.mp4
   |   |_ 3K0Sw7rbzPU_000114_000124.mp4
   |   |_ 6Tnsmk9C2rg_000048_000058.mp4
   |   |_ ...

  3. Run the scripts:

    python hailo_model_zoo/datasets/create_kinetics400_tfrecord.py calib --data <path_to_k400/videos>
python hailo_model_zoo/datasets/create_kinetics400_tfrecord.py val --data <path_to_k400/videos>

NUSCENES

  1. Download the dataset from here and extract.

  2. The expected dataset structure:

    nuscenes
|_ maps
|  |_ *.png
|_ samples
|  |_ CAM_BACK
|  |_ |_ *.jpg
|  |_ CAM_BACK_LEFT
|  |_ |_ *.jpg
|  |_ CAM_BACK_RIGHT
|  |_ |_ *.jpg
|  |_ CAM_FRONT
|  |_ |_ *.jpg
|  |_ CAM_FRONT_LEFT
|  |_ |_ *.jpg
|  |_ CAM_FRONT_RIGHT
|  |_ |_ *.jpg
|_ sweeps
|  |_ CAM_BACK
|  |_ |_ *.jpg
|  |_ CAM_BACK_LEFT
|  |_ |_ *.jpg
|  |_ CAM_BACK_RIGHT
|  |_ |_ *.jpg
|  |_ CAM_FRONT
|  |_ |_ *.jpg
|  |_ CAM_FRONT_LEFT
|  |_ |_ *.jpg
|  |_ CAM_FRONT_RIGHT
|  |_ |_ *.jpg
|_ v1.0-trainval
|  |_ *.json

  3. Run the scripts:

    python hailo_model_zoo/datasets/create_nuscenes_petrv2_cascade_tfrecord.py calib --ann_file <train_annotation_file.pkl> --coords-dir <coords3d_directory_path>
python hailo_model_zoo/datasets/create_nuscenes_petrv2_cascade_tfrecord.py val --ann_file <val_annotation_file.pkl> --coords-dir <coords3d_directory_path>

    Where <*_annotation_file.pkl> is the train / val .pkl annotation file generated from the PETR training environment.

    Notice: In order to benchmark our PETRv2 cascade (petrv2), please download the annotation .pkl file from here and create a symbolic link (softlink) from /fastdata/data/nuscenes/nuesence/ to your nuscenes dataset folder.