references.zip

# 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 ``` torchrun --nproc_per_node=8 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 ``` 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 ``` ### Faster R-CNN MobileNetV3-Large 320 FPN ``` 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 ``` ### RetinaNet ``` 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 ``` ### SSD300 VGG16 ``` 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 ``` ### SSDlite320 MobileNetV3-Large ``` 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 ``` ### Mask R-CNN ``` torchrun --nproc_per_node=8 train.py\ --dataset coco --model maskrcnn_resnet50_fpn --epochs 26\ --lr-steps 16 22 --aspect-ratio-group-factor 3 ``` ### Keypoint R-CNN ``` 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 ```