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.
torchrun --nproc_per_node=8 train.py\
--dataset coco --model fasterrcnn_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py\
--dataset coco --model fasterrcnn_mobilenet_v3_large_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py\
--dataset coco --model fasterrcnn_mobilenet_v3_large_320_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py\
--dataset coco --model fcos_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01 --amp --weights-backbone ResNet50_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py\
--dataset coco --model retinanet_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py\
--dataset coco --model ssd300_vgg16 --epochs 120\
--lr-steps 80 110 --aspect-ratio-group-factor 3 --lr 0.002 --batch-size 4\
--weight-decay 0.0005 --data-augmentation ssd --weights-backbone VGG16_Weights.IMAGENET1K_FEATURES
torchrun --nproc_per_node=8 train.py\
--dataset coco --model ssdlite320_mobilenet_v3_large --epochs 660\
--aspect-ratio-group-factor 3 --lr-scheduler cosineannealinglr --lr 0.15 --batch-size 24\
--weight-decay 0.00004 --data-augmentation ssdlite
torchrun --nproc_per_node=8 train.py\
--dataset coco --model maskrcnn_resnet50_fpn --epochs 26\
--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
torchrun --nproc_per_node=8 train.py\
--dataset coco_kp --model keypointrcnn_resnet50_fpn --epochs 46\
--lr-steps 36 43 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1