Table Of Contents
Table Of Contents

Depth Prediction

Here is the model zoo for task of depth prediction.

Hint

Training commands work with this script: Download train.py

The test script Download test.py can be used for evaluating the models on various datasets.

KITTI Dataset

The following table lists pre-trained models trained on KITTI.

Hint

The test script Download test.py can be used for evaluating the models (KITTI RAW results are evaluated using the official server). For example monodepth2_resnet18_kitti_stereo_640x192:

python test.py --model_zoo monodepth2_resnet18_kitti_stereo_640x192 --pretrained_type gluoncv --eval_stereo --png

Note

Our pre-trained models reproduce results from recent state-of-the-art approaches. Please check the reference paper for further information.

Modality is the method used during training. Stereo means we use stereo image pairs to calculate the loss.

Resolution is the input size of the model during training. 640x192 means we resize the raw image (1242x375) to 640x192.

Name

Modality

Resolution

Abs. Rel. Error

delta < 1.25

Hashtag

Train Command

Train Log

monodepth2_resnet18_kitti_stereo_640x192 1

Stereo

640x192

0.114

0.856

92871317

shell script

log

1

Clement Godard, Oisin Mac Aodha, Michael Firman and Gabriel J. Brostow. “Digging into Self-Supervised Monocular Depth Prediction.” Proceedings of the International Conference on Computer Vision (ICCV), 2019.

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