INRIA Pedestrian

The INRIA person dataset is popular in the Pedestrian Detection community, both for training detectors and reporting results.

It consists of 614 person detections for training and 288 for testing.

Note

The data files available for download are the ones distributed in here.

Use cases

Pedestrian detection in images.

Properties

  • name: inria_pedestrian
  • keywords: image_processing, detection, pedestrian
  • dataset size: 1,1 GB
  • is downloadable: yes
  • tasks:
    • detection: (default)
      • primary use: object detection
      • description: Contains image filenames, classes and bounding box annotations for pedestrian detection in images/videos.
      • sets: train, test
      • metadata file size in disk: 139,1 kB
      • has annotations: yes
        • which:
          • labels for each class/category.
          • bounding box of pedestrians.
          • occlusion % of annotated pedestrians.

Metadata structure (HDF5)

Task: detection

/
├── train/
│   ├── image_filenames   # dtype=np.uint8, shape=(1832,88)  (note: string in ASCII format)
│   ├── classes           # dtype=np.uint8, shape=(4,10)     (note: string in ASCII format)
│   ├── boxes             # dtype=np.float, shape=(1237,4)
│   ├── boxesv            # dtype=np.float, shape=(1237,4)
│   ├── id                # dtype=np.int32, shape=(1237,)
│   ├── occlusion         # dtype=np.float, shape=(1237,)
│   ├── object_fields     # dtype=np.uint8, shape=(6,16)     (note: string in ASCII format)
│   ├── object_ids        # dtype=np.int32, shape=(1237,6)
│   ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,614))
│   ├── list_boxes_per_image             # dtype=np.int32, shape=(1832,12))
│   ├── list_boxesv_per_image            # dtype=np.int32, shape=(1832,12))
│   ├── list_object_ids_per_image        # dtype=np.int32, shape=(1832,12))
│   └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,1237))
│
└── test/
    ├── image_filenames   # dtype=np.uint8, shape=(741,88)  (note: string in ASCII format)
    ├── classes           # dtype=np.uint8, shape=(4,10)     (note: string in ASCII format)
    ├── boxes             # dtype=np.float, shape=(589,4)
    ├── boxesv            # dtype=np.float, shape=(589,4)
    ├── id                # dtype=np.int32, shape=(589,)
    ├── occlusion         # dtype=np.float, shape=(589,)
    ├── object_fields     # dtype=np.uint8, shape=(6,16)     (note: string in ASCII format)
    ├── object_ids        # dtype=np.int32, shape=(589,6)
    ├── list_image_filenames_per_class   # dtype=np.int32, shape=(4,288))
    ├── list_boxes_per_image             # dtype=np.int32, shape=(741,16))
    ├── list_boxesv_per_image            # dtype=np.int32, shape=(741,16))
    ├── list_object_ids_per_image        # dtype=np.int32, shape=(741,16))
    └── list_objects_ids_per_class       # dtype=np.int32, shape=(4,589))

Fields

  • 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
  • classes: class names
    • available in: train, test
    • dtype: np.uint8
    • is padded: True
    • fill value: 0
    • note: strings stored in ASCII format
  • 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)
  • id: label ids
    • available in: train, test
    • dtype: np.int32
    • is padded: False
    • fill value: -1
  • occlusion: occlusion percentage
    • available in: train, test
    • dtype: np.float
    • is padded: False
    • fill value: -1
  • 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)
  • 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_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_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 INRIA Pedestrian Dataset.

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