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This folder contains reference training scripts for object detection. They serve as a log of how to train specific models, to provide baseline training and evaluation scripts to quickly bootstrap research.
To execute the example commands below you must install the following:
cython
pycocotools
matplotlib
You must modify the following flags:
--data-path=/path/to/coco/dataset
--nproc_per_node=<number_of_gpus_available>
Except otherwise noted, all models have been trained on 8x V100 GPUs.
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
--dataset coco --model fasterrcnn_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
--dataset coco --model retinanet_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
--dataset coco --model maskrcnn_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3
python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
--dataset coco_kp --model keypointrcnn_resnet50_fpn --epochs 46\
--lr-steps 36 43 --aspect-ratio-group-factor 3
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