Caltech Pedestrian

The Caltech Pedestrian Dataset consists of approximately 10 hours of 640x480 30Hz video taken from a vehicle driving through regular traffic in an urban environment. About 250,000 frames (in 137 approximately minute long segments) with a total of 350,000 bounding boxes and 2300 unique pedestrians were annotated.

The annotation includes temporal correspondence between bounding boxes and detailed occlusion labels.

Use cases

Pedestrian detection in images/videos.

Properties

Note

The detection tasks contains 1/30 of all frames of each video.

The detection_10x tasks contains 1/3 of all frames of each video.

The detection_30x tasks has all the frames of each video.

Tasks ending with _clean have bounding boxes with small area (less than 5px width/height) discarded. These are mostly due to bad annotations and are kept from these tasks.

Tasks

detection (default)

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> caltech_ped = dbc.load('caltech_pedestrian')
>>> caltech_ped
DataLoader: "caltech_pedestrian" (detection task)

Properties

HDF5 file structure

/
├── train/
│   ├── boxes                    # dtype=np.float, shape=(6365,4)
│   ├── boxesv                   # dtype=np.float, shape=(6365,4)
│   ├── classes                  # dtype=np.uint8, shape=(6365,10)  (note: string in ASCII format)
│   ├── classes_unique           # dtype=np.uint8, shape=(4,10)     (note: string in ASCII format)
│   ├── id                       # dtype=np.int32, shape=(6365,)
│   ├── image_filenames          # dtype=np.uint8, shape=(6365,90)  (note: string in ASCII format)
│   ├── image_filenames_unique   # dtype=np.uint8, shape=(4250,90)  (note: string in ASCII format)
│   ├── object_fields            # dtype=np.uint8, shape=(6,16)     (note: string in ASCII format)
│   ├── object_ids               # dtype=np.int32, shape=(6365,6)
│   ├── occlusion                # dtype=np.float, shape=(6365,)
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(2223,22)
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(2223,22)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,2033)
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(2223,22)
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,5081)
│
└── test/
    ├── boxes                    # dtype=np.float, shape=(5142,4)
    ├── boxesv                   # dtype=np.float, shape=(5142,4)
    ├── classes                  # dtype=np.uint8, shape=(5142,10)  (note: string in ASCII format)
    ├── classes_unique           # dtype=np.uint8, shape=(4,10)     (note: string in ASCII format)
    ├── id                       # dtype=np.int32, shape=(5142,)
    ├── image_filenames          # dtype=np.uint8, shape=(5142,90)  (note: string in ASCII format)
    ├── image_filenames_unique   # dtype=np.uint8, shape=(4024,90)  (note: string in ASCII format)
    ├── object_fields            # dtype=np.uint8, shape=(6,16)     (note: string in ASCII format)
    ├── object_ids               # dtype=np.int32, shape=(5142,6)
    ├── occlusion                # dtype=np.float, shape=(5142,)
    ├── list_boxes_per_image             # dtype=np.int32, shape=(2152,13)
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(2152,13)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,2014)
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(2152,13)
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,4401)

Fields

  • boxes: bounding boxes
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • boxesv: bounding boxes (visible)
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • classes: unique class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • image_filenames: image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • image_filenames: unique image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • list_boxes_per_image: list of bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_boxesv_per_image: list of (visible) bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_image_filenames_per_class: list of image per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_object_ids_per_image: list of object ids per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_objects_ids_per_class: list of object ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

detection_clean

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> caltech_ped_clean = dbc.load('caltech_pedestrian', 'detection_clean')
>>> caltech_ped_clean
DataLoader: "caltech_pedestrian" (detection_clean task)

Properties

HDF5 file structure

/
├── train/
│   ├── boxes                    # dtype=np.float, shape=(6313,4)
│   ├── boxesv                   # dtype=np.float, shape=(6313,4)
│   ├── classes                  # dtype=np.uint8, shape=(6313,10)  (note: string in ASCII format)
│   ├── classes_unique           # dtype=np.uint8, shape=(4,10)     (note: string in ASCII format)
│   ├── id                       # dtype=np.int32, shape=(6313,)
│   ├── image_filenames          # dtype=np.uint8, shape=(6313,90)  (note: string in ASCII format)
│   ├── image_filenames_unique   # dtype=np.uint8, shape=(4250,90)  (note: string in ASCII format)
│   ├── object_fields            # dtype=np.uint8, shape=(6,16)     (note: string in ASCII format)
│   ├── object_ids               # dtype=np.int32, shape=(6313,6)
│   ├── occlusion                # dtype=np.float, shape=(6313,)
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(2218,22)
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(2218,22)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,2027)
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(2218,22)
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,5033)
│
└── test/
    ├── boxes                    # dtype=np.float, shape=(5109,4)
    ├── boxesv                   # dtype=np.float, shape=(5109,4)
    ├── classes                  # dtype=np.uint8, shape=(5109,10)  (note: string in ASCII format)
    ├── classes_unique           # dtype=np.uint8, shape=(4,10)     (note: string in ASCII format)
    ├── id                       # dtype=np.int32, shape=(5109,)
    ├── image_filenames          # dtype=np.uint8, shape=(5109,90)  (note: string in ASCII format)
    ├── image_filenames_unique   # dtype=np.uint8, shape=(4024,90)  (note: string in ASCII format)
    ├── object_fields            # dtype=np.uint8, shape=(6,16)     (note: string in ASCII format)
    ├── object_ids               # dtype=np.int32, shape=(5109,6)
    ├── occlusion                # dtype=np.float, shape=(5109,)
    ├── list_boxes_per_image             # dtype=np.int32, shape=(2148,13)
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(2148,13)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,2010)
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(2148,13)
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,4371)

Fields

  • boxes: bounding boxes
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • boxesv: bounding boxes (visible)
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • classes: unique class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • image_filenames: image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • image_filenames: unique image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • list_boxes_per_image: list of bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_boxesv_per_image: list of (visible) bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_image_filenames_per_class: list of image per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_object_ids_per_image: list of object ids per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_objects_ids_per_class: list of object ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

detection_10x

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> caltech_ped_10x = dbc.load('caltech_pedestrian', 'detection_10x')
>>> caltech_ped_10x
DataLoader: "caltech_pedestrian" (detection_10x task)

Properties

HDF5 file structure

/
├── train/
│   ├── boxes                    # dtype=np.float, shape=(64063,4)
│   ├── boxesv                   # dtype=np.float, shape=(64063,4)
│   ├── classes                  # dtype=np.uint8, shape=(64063,10)  (note: string in ASCII format)
│   ├── classes_unique           # dtype=np.uint8, shape=(4,10)      (note: string in ASCII format)
│   ├── id                       # dtype=np.int32, shape=(64063,)
│   ├── image_filenames          # dtype=np.uint8, shape=(64063,90)  (note: string in ASCII format)
│   ├── image_filenames_unique   # dtype=np.uint8, shape=(42782,90)  (note: string in ASCII format)
│   ├── object_fields            # dtype=np.uint8, shape=(6,16)      (note: string in ASCII format)
│   ├── object_ids               # dtype=np.int32, shape=(64063,6)
│   ├── occlusion                # dtype=np.float, shape=(64063,)
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(22356,22)
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(22356,22)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,20480)
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(22356,22)
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,51092)
│
└── test/
    ├── boxes                    # dtype=np.float, shape=(51451,4)
    ├── boxesv                   # dtype=np.float, shape=(51451,4)
    ├── classes                  # dtype=np.uint8, shape=(51451,10)  (note: string in ASCII format)
    ├── classes_unique           # dtype=np.uint8, shape=(4,10)      (note: string in ASCII format)
    ├── id                       # dtype=np.int32, shape=(51451,)
    ├── image_filenames          # dtype=np.uint8, shape=(51451,90)  (note: string in ASCII format)
    ├── image_filenames_unique   # dtype=np.uint8, shape=(40465,90)  (note: string in ASCII format)
    ├── object_fields            # dtype=np.uint8, shape=(6,16)      (note: string in ASCII format)
    ├── object_ids               # dtype=np.int32, shape=(51451,6)
    ├── occlusion                # dtype=np.float, shape=(51451,)
    ├── list_boxes_per_image             # dtype=np.int32, shape=(21653,14)
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(21653,14)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,20239)
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(21653,14)
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,44095)

Fields

  • boxes: bounding boxes
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • boxesv: bounding boxes (visible)
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • classes: unique class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • image_filenames: image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • image_filenames: unique image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • list_boxes_per_image: list of bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_boxesv_per_image: list of (visible) bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_image_filenames_per_class: list of image per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_object_ids_per_image: list of object ids per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_objects_ids_per_class: list of object ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

detection_10x_clean

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> caltech_ped_10x_clean = dbc.load('caltech_pedestrian', 'detection_10x_clean')
>>> caltech_ped_10x_clean
DataLoader: "caltech_pedestrian" (detection_10x_clean task)

Properties

HDF5 file structure

/
├── train/
│   ├── boxes                    # dtype=np.float, shape=(63538,4)
│   ├── boxesv                   # dtype=np.float, shape=(63538,4)
│   ├── classes                  # dtype=np.uint8, shape=(63538,10)  (note: string in ASCII format)
│   ├── classes_unique           # dtype=np.uint8, shape=(4,10)      (note: string in ASCII format)
│   ├── id                       # dtype=np.int32, shape=(63538,)
│   ├── image_filenames          # dtype=np.uint8, shape=(63538,90)  (note: string in ASCII format)
│   ├── image_filenames_unique   # dtype=np.uint8, shape=(42782,90)  (note: string in ASCII format)
│   ├── object_fields            # dtype=np.uint8, shape=(6,16)      (note: string in ASCII format)
│   ├── object_ids               # dtype=np.int32, shape=(63538,6)
│   ├── occlusion                # dtype=np.float, shape=(63538,)
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(22303,22)
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(22303,22)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,20422)
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(22303,22)
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,50605)
│
└── test/
    ├── boxes                    # dtype=np.float, shape=(51079,4)
    ├── boxesv                   # dtype=np.float, shape=(51079,4)
    ├── classes                  # dtype=np.uint8, shape=(51079,10)  (note: string in ASCII format)
    ├── classes_unique           # dtype=np.uint8, shape=(4,10)      (note: string in ASCII format)
    ├── id                       # dtype=np.int32, shape=(51079,)
    ├── image_filenames          # dtype=np.uint8, shape=(51079,90)  (note: string in ASCII format)
    ├── image_filenames_unique   # dtype=np.uint8, shape=(40465,90)  (note: string in ASCII format)
    ├── object_fields            # dtype=np.uint8, shape=(6,16)      (note: string in ASCII format)
    ├── object_ids               # dtype=np.int32, shape=(51079,6)
    ├── occlusion                # dtype=np.float, shape=(51079,)
    ├── list_boxes_per_image             # dtype=np.int32, shape=(21590,14)
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(21590,14)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,20173)
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(21590,14)
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,43748)

Fields

  • boxes: bounding boxes
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • boxesv: bounding boxes (visible)
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • classes: unique class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • image_filenames: image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • image_filenames: unique image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • list_boxes_per_image: list of bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_boxesv_per_image: list of (visible) bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_image_filenames_per_class: list of image per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_object_ids_per_image: list of object ids per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_objects_ids_per_class: list of object ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

detection_30x

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> caltech_ped_30x = dbc.load('caltech_pedestrian', 'detection_30x')
>>> caltech_ped_30x
DataLoader: "caltech_pedestrian" (detection_30x task)

Properties

HDF5 file structure

/
├── train/
│   ├── boxes                    # dtype=np.float, shape=(192185,4)
│   ├── boxesv                   # dtype=np.float, shape=(192185,4)
│   ├── classes                  # dtype=np.uint8, shape=(192185,10)  (note: string in ASCII format)
│   ├── classes_unique           # dtype=np.uint8, shape=(4,10)       (note: string in ASCII format)
│   ├── id                       # dtype=np.int32, shape=(192185,)
│   ├── image_filenames          # dtype=np.uint8, shape=(192185,90)  (note: string in ASCII format)
│   ├── image_filenames_unique   # dtype=np.uint8, shape=(128419,90)  (note: string in ASCII format)
│   ├── object_fields            # dtype=np.uint8, shape=(6,16)       (note: string in ASCII format)
│   ├── object_ids               # dtype=np.int32, shape=(192185,6)
│   ├── occlusion                # dtype=np.float, shape=(192185,)
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(67083,22)
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(67083,22)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,61439)
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(67083,22)
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,153234)
│
└── test/
    ├── boxes                    # dtype=np.float, shape=(154436,4)
    ├── boxesv                   # dtype=np.float, shape=(154436,4)
    ├── classes                  # dtype=np.uint8, shape=(154436,10)  (note: string in ASCII format)
    ├── classes_unique           # dtype=np.uint8, shape=(4,10)       (note: string in ASCII format)
    ├── id                       # dtype=np.int32, shape=(154436,)
    ├── image_filenames          # dtype=np.uint8, shape=(154436,90)  (note: string in ASCII format)
    ├── image_filenames_unique   # dtype=np.uint8, shape=(121465,90)  (note: string in ASCII format)
    ├── object_fields            # dtype=np.uint8, shape=(6,16)       (note: string in ASCII format)
    ├── object_ids               # dtype=np.int32, shape=(154436,6)
    ├── occlusion                # dtype=np.float, shape=(154436,)
    ├── list_boxes_per_image             # dtype=np.int32, shape=(64999,14)
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(64999,14)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,60748)
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(64999,14)
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,132324)

Fields

  • boxes: bounding boxes
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • boxesv: bounding boxes (visible)
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • classes: unique class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • image_filenames: image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • image_filenames: unique image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • list_boxes_per_image: list of bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_boxesv_per_image: list of (visible) bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_image_filenames_per_class: list of image per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_object_ids_per_image: list of object ids per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_objects_ids_per_class: list of object ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

detection_30x_clean

How to use

>>> # import the package
>>> import dbcollection as dbc
>>>
>>> # load the dataset
>>> caltech_ped_30x_clean = dbc.load('caltech_pedestrian', 'detection_30x_clean')
>>> caltech_ped_30x_clean
DataLoader: "caltech_pedestrian" (detection_30x_clean task)

Properties

HDF5 file structure

/
├── train/
│   ├── boxes                    # dtype=np.float, shape=(190598,4)
│   ├── boxesv                   # dtype=np.float, shape=(190598,4)
│   ├── classes                  # dtype=np.uint8, shape=(190598,10)  (note: string in ASCII format)
│   ├── classes_unique           # dtype=np.uint8, shape=(4,10)       (note: string in ASCII format)
│   ├── id                       # dtype=np.int32, shape=(190598,)
│   ├── image_filenames          # dtype=np.uint8, shape=(190598,90)  (note: string in ASCII format)
│   ├── image_filenames_unique   # dtype=np.uint8, shape=(128419,90)  (note: string in ASCII format)
│   ├── object_fields            # dtype=np.uint8, shape=(6,16)       (note: string in ASCII format)
│   ├── object_ids               # dtype=np.int32, shape=(190598,6)
│   ├── occlusion                # dtype=np.float, shape=(190598,)
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(66923,22)
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(66923,22)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,61274)
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(66923,22)
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,151768)
│
└── test/
    ├── boxes                    # dtype=np.float, shape=(153305,4)
    ├── boxesv                   # dtype=np.float, shape=(153305,4)
    ├── classes                  # dtype=np.uint8, shape=(153305,10)  (note: string in ASCII format)
    ├── classes_unique           # dtype=np.uint8, shape=(4,10)       (note: string in ASCII format)
    ├── id                       # dtype=np.int32, shape=(153305,)
    ├── image_filenames          # dtype=np.uint8, shape=(153305,90)  (note: string in ASCII format)
    ├── image_filenames_unique   # dtype=np.uint8, shape=(121465,90)  (note: string in ASCII format)
    ├── object_fields            # dtype=np.uint8, shape=(6,16)       (note: string in ASCII format)
    ├── object_ids               # dtype=np.int32, shape=(153305,6)
    ├── occlusion                # dtype=np.float, shape=(153305,)
    ├── list_boxes_per_image             # dtype=np.int32, shape=(64801,14)
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(64801,14)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,60537)
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(64801,14)
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,131273)

Fields

  • boxes: bounding boxes
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • boxesv: bounding boxes (visible)
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
    • note: bbox format (x1,y1,x2,y2)
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • classes: unique class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • image_filenames: image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • image_filenames: unique image file path + names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • object_fields: list of field names of the object id list
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
    • note: key field (field name aggregator)
  • object_ids: list of field ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
    • note: key field (field id aggregator)
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • list_boxes_per_image: list of bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_boxesv_per_image: list of (visible) bounding boxes per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_image_filenames_per_class: list of image per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_object_ids_per_image: list of object ids per image
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list
  • list_objects_ids_per_class: list of object ids per class
    • available in: train, test
    • dtype: np.int32
    • is padded: True
    • fill value: -1
    • note: pre-ordered list

Disclaimer

All rights reserved to the original creators of Caltech Pedestrian Dataset.

For information about the dataset and its terms of use, please see this link.