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# 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
```
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