ViPeD_19: YOLO detector trained using JTA

CVPR19-04


Short name:

ViPeD_19

Description:

n/a

Project page / code:

n/a

Reference:

G. Amato, L. Ciampi, F. Falchi, C. Gennaro, N. Messina. Learning pedestrian detection from virtual worlds. In International Conference on Image Analysis and Processing, 2019.

Last submitted:

June 13, 2019 (11 months ago)

Published:

July 05, 2019 at 23:52:29 CET

Submissions:

1

Open source:

Yes

Hardware:

RTX 2080Ti

Runtime:

11.2 Hz

Benchmark performance:

Sequence AP MODA MODP FAF TP FP FN Recall Precision F1
CVPR 2019 Detection Challenge0.7339.071.535.7308,545159,98371,99781.165.972.7

Detailed performance:

Sequence AP MODA MODP FAF TP FP FN Recall Precision F1
CVPR19-040.7757.471.032.3221,66567,19147,28282.476.779.5
CVPR19-060.65-7.872.451.046,53951,40916,18674.247.557.9
CVPR19-070.8965.974.97.014,8584,1091,46091.178.384.2
CVPR19-080.61-36.271.746.225,48337,2747,06978.340.653.5

Raw data: