DETR is an object detection model based on transformer. We reproduced the model of the paper.
Backbone | Model | Images/GPU | Inf time (fps) | Box AP | Config | Download |
---|---|---|---|---|---|---|
R-50 | DETR | 4 | --- | 42.3 | config | model |
Notes:
- DETR is trained on COCO train2017 dataset and evaluated on val2017 results of
mAP(IoU=0.5:0.95)
. - DETR uses 8GPU to train 500 epochs.
GPU multi-card training
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/detr/detr_r50_1x_coco.yml --fleet
@inproceedings{detr,
author = {Nicolas Carion and
Francisco Massa and
Gabriel Synnaeve and
Nicolas Usunier and
Alexander Kirillov and
Sergey Zagoruyko},
title = {End-to-End Object Detection with Transformers},
booktitle = {ECCV},
year = {2020}
}