Object detection reference training scripts

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.

Faster R-CNN ResNet-50 FPN

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

Faster R-CNN MobileNetV3-Large FPN

python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
    --dataset coco --model fasterrcnn_mobilenet_v3_large_fpn --epochs 26\
    --lr-steps 16 22 --aspect-ratio-group-factor 3

Faster R-CNN MobileNetV3-Large 320 FPN

python -m torch.distributed.launch --nproc_per_node=8 --use_env train.py\
    --dataset coco --model fasterrcnn_mobilenet_v3_large_320_fpn --epochs 26\
    --lr-steps 16 22 --aspect-ratio-group-factor 3

RetinaNet

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

Mask R-CNN

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

Keypoint R-CNN

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