Recordings and Datasets
Sample recordings
Those recordings are available in multiple formats: RAW (EVT2 or EVT3), DAT and HDF5 event files.
Note
Those files are shared under the Creative Commons Zero v1.0 Universal (CC0 1.0) license. This means that you are free to copy, modify, distribute, and use the data for any purpose, including commercial purposes, without asking permission.
For more information, you can refer to the full license details.
Name |
Screenshot |
Description |
Sensor |
Duration |
Event Rate (Mev/s) |
Recordings |
|---|---|---|---|---|---|---|
spinner |
|
Turning Spinner |
Gen3.0 |
5 s |
10.8 |
|
hand_spinner |
|
Turning 3-Blade Hand Spinner |
Gen3.1 |
5 s |
2.4 |
|
laser |
|
High-speed laser motion |
Gen4.1 |
3 s |
38.8 |
|
80_balls |
|
Falling 80 balls (to use with Counting Sample) |
Gen3.0 |
6.3 s |
0.73 |
|
195_falling_particles |
|
Falling 195 particles of different sizes (synthetic data) (to use with PSM Sample) |
Gen3.1 |
32 ms |
322.4 |
|
industrial_fluid_flow |
|
Particles suspended and moving within a fluid flow |
Gen4.0 |
5.6 s |
1.9 |
|
industrial_spray |
|
A cloud of spray gas being ejected into the air |
Gen3.1 |
4.3 s |
1.3 |
|
monitoring_40_50hz |
|
Object vibration at 40 and 50Hz (to use with Vibration Estimation Sample) |
Gen3.0 |
6 s |
6.8 |
|
sparklers |
|
High-speed scatter-like motion of small particles |
Gen3.0 |
95 ms |
5.4 |
|
200_jets_at_200hz |
|
200 horizontal fluid jets (to use with Jet Monitoring Sample) |
Gen3.1 |
2 s |
0.16 |
|
pedestrians |
|
Pedestrians in multiple directions |
Gen4.1 |
30 s |
1.3 |
|
traffic_monitoring |
|
Cars moving on a highway (static camera) |
Gen3.0 |
28 s |
0.64 |
|
driving_sample |
|
Driving on a street road (moving camera) |
Gen4.1 |
12.5 s |
8.9 |
|
eye_tracking |
|
Right eye with moving pupil and closing eyelid |
Gen4.0 |
9.9 s |
5.7 |
|
box |
|
Camera moving around a box (to use with metavision_model_3d_tracking sample) |
Gen3.1 |
28.6 s |
7.8 |
|
cube |
|
Camera moving around a cube (to use with metavision_model_3d_tracking sample) |
Gen3.1 |
17.7 s |
8.29 |
|
marker |
|
Camera moving around a marker (to use with metavision_model_3d_tracking sample) |
Gen3.1 |
22.3 s |
4.9 |
|
active_marker |
|
Active Marker moving in front of the camera (to use with Active Marker samples: 2D tracking and 3D tracking) |
IMX636 |
31.6 s |
0.60 |
|
aruco_marker |
|
ArUco marker moving in front of the camera |
IMX636 |
16.2 s |
25.5 |
|
courtyard_walk_stereo |
|
Walk in a courtyard recorded using a stereo setup (to use with Stereo Matching Sample) |
GenX320 |
26.0 s |
1.7 |
Datasets
Dataset |
Application |
Sensor |
Resolution |
Size |
Format |
|---|---|---|---|---|---|
Automotive detection |
Gen4.0 |
1280×720 |
1.6TB compressed 3.5TB uncompressed |
Recording in DAT format Labels in numpy format |
|
Corner detection |
Gen3.0 ATIS |
480×360 |
6GB compressed 11GB uncompressed |
Recording in DAT format |
|
Car classification |
Gen1.0 |
crops from 304×240 |
280MB compressed |
Recording in DAT format |
|
Automotive detection |
Gen1.0 |
304×240 |
200GB compressed 750GB uncompressed |
Recording in DAT format Labels in numpy format |
|
Gen4.0 |
1280×720 |
24GB uncompressed |
Recording in DAT format Labels in numpy format |
||
Gesture classification |
Gen3.1 |
640x480 |
113GB uncompressed |
Recording in DAT format Labels in numpy format |
Precomputed Datasets
Those datasets were precompiled in an HDF5 tensor file format for faster training and smaller disk usage.
Dataset |
Preprocessing |
Sensor |
Resolution |
Size |
Format |
|---|---|---|---|---|---|
Gen4.0 |
1280×720 down to 640x360 |
211.3GB |
Labels in numpy format |
||
Multi Channel Time Surface |
Gen4.0 |
1280×720 down to 640x360 |
288.4GB |
Labels in numpy format |
|
Gen4.0 |
1280×720 down to 640x360 |
86GB |
Labels in numpy format |
||
Gen1.0 |
304×240 |
59.3GB |
Labels in numpy format |
||
Histogram quantized |
Gen3.1 |
640x480 |
3.2GB |
Labels in numpy format |