Datasets
All datasets are subclasses of tonic.datasets.Dataset
and need certain methods implemented: __init__
, __getitem__
and __len__
. This design is inspired by torchvision’s way to provide datasets.
Events for a sample in both audio and vision datasets are output as structured numpy arrays of shape (N,E), where N is the number of events and E is the number of event channels. Vision events typically have 4 event channels: time, x and y pixel coordinates and polarity, whereas audio events typically have time, x and polarity.
Visual event stream classification
ASLDVS (save_to[, transform, ...])
|
ASL-DVS |
CIFAR10DVS (save_to[, transform, ...])
|
CIFAR10-DVS |
DVSGesture (save_to[, train, transform, ...])
|
IBM DVS Gestures |
NCALTECH101 (save_to[, transform, ...])
|
N-CALTECH101 |
NMNIST (save_to[, train, first_saccade_only, ...])
|
N-MNIST |
POKERDVS (save_to[, train, transform, ...])
|
POKER-DVS |
SMNIST (save_to[, train, duplicate, ...])
|
Spiking sequential MNIST |
DVSLip (save_to[, train, transform, ...])
|
DVS-Lip |
Audio event stream classification
Pose estimation, visual odometry, SLAM
DAVISDATA (save_to, recording[, transform, ...])
|
DAVIS event camera dataset |
DSEC (save_to, split, data_selection[, ...])
|
DSEC |
MVSEC (save_to, scene[, transform, ...])
|
MVSEC |
TUMVIE (save_to, recording[, transform, ...])
|
TUM-VIE |
VPR (save_to[, transform, target_transform, ...])
|
Visual Place Recognition |
Prototype iterable datasets
NMNIST (root[, train, first_saccade_only, ...])
|
N-MNIST
|
NCARS (root[, train, skip_sha256_check])
|
N-CARS |
STMNIST (root[, keep_compressed, ...])
|
ST-MNIST |
Gen1AutomotiveDetection (root[, split, shuffle])
|
Gen1 Automotive Detection Dataset |
Gen4AutomotiveDetectionMini (root[, split, ...])
|
Gen4 Automotive Detection |