This repo provides the evaluation codes used in our ICCV 2019 paper A Delay Metric for Video Object Detection: What Average Precision Fails to Tell, including:
- Mean Average Precision (mAP)
- Average Delay (AD)
- A redesigned NAB metric for the video object detection problem.
Prepare the data
Download the groundtruth annotations and the sample detector outputs from Google Drive.
The groundtruth annotations of VIDT are stored in KITTI-format due to its simplicity and io-efficiency.
We provide the outputs of the following methods. The github repos that generate those outputs are also listed.
All the evaluation scripts are under
./experiments folder. For instance, to measure the mAP and AD of FGFA, run command:
python experiments/eval_map_ad.py examples/rfcn_fgfa_7 data/ILSVRC2015_KITTI_FORMAT
Evaluate your own detector.
For every video sequence, output a file as
<sequence_name>.txt. Each line in the file should be one single object in
<frame_id> <class_id> <confidence> <xmin> <ymin> <xmax> <ymax> format.
This pure Python-based mAP evaluation code is refactored from Cartucho/mAP. It has been tested against the original matlab version.