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_pedestriankeywords
: image_processing, detection, pedestriandataset size
: 1,1 GBis downloadable
: yestasks
:- detection: (default)
primary use
: object detectiondescription
: Contains image filenames, classes and bounding box annotations for pedestrian detection in images/videos.sets
: train, testmetadata file size in disk
: 139,1 kBhas annotations
: yeswhich
:- 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+namesavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII format
classes
: class namesavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII format
boxes
: bounding boxesavailable in
: train, testdtype
: np.floatis padded
: Falsefill value
: -1note
: bbox format (x1,y1,x2,y2)
boxesv
: bounding boxes (visible)available in
: train, testdtype
: np.floatis padded
: Falsefill value
: -1note
: bbox format (x1,y1,x2,y2)
id
: label idsavailable in
: train, testdtype
: np.int32is padded
: Falsefill value
: -1
occlusion
: occlusion percentageavailable in
: train, testdtype
: np.floatis padded
: Falsefill value
: -1
object_fields
: list of field names of the object id listavailable in
: train, testdtype
: np.uint8is padded
: Truefill value
: 0note
: strings stored in ASCII formatnote
: key field (field name aggregator)
object_ids
: list of field idsavailable in
: train, testdtype
: np.int32is padded
: Falsefill value
: -1note
: key field (field id aggregator)
list_image_filenames_per_class
: list of image per classavailable in
: train, testdtype
: np.int32is padded
: Truefill value
: -1note
: pre-ordered list
list_boxes_per_image
: list of bounding boxes per imageavailable in
: train, testdtype
: np.int32is padded
: Truefill value
: -1note
: pre-ordered list
list_boxesv_per_image
: list of (visible) bounding boxes per imageavailable in
: train, testdtype
: np.int32is padded
: Truefill value
: -1note
: pre-ordered list
list_object_ids_per_image
: list of object ids per imageavailable in
: train, testdtype
: np.int32is padded
: Truefill value
: -1note
: pre-ordered list
list_objects_ids_per_class
: list of object ids per classavailable in
: train, testdtype
: np.int32is padded
: Truefill value
: -1note
: pre-ordered list