89 lines
2.8 KiB
Markdown
89 lines
2.8 KiB
Markdown
# Object detection reference training scripts
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This folder contains reference training scripts for object detection.
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They serve as a log of how to train specific models, to provide baseline
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training and evaluation scripts to quickly bootstrap research.
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To execute the example commands below you must install the following:
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```
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cython
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pycocotools
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matplotlib
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```
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You must modify the following flags:
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`--data-path=/path/to/coco/dataset`
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`--nproc_per_node=<number_of_gpus_available>`
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Except otherwise noted, all models have been trained on 8x V100 GPUs.
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### Faster R-CNN ResNet-50 FPN
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model fasterrcnn_resnet50_fpn --epochs 26\
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--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
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```
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### Faster R-CNN MobileNetV3-Large FPN
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model fasterrcnn_mobilenet_v3_large_fpn --epochs 26\
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--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
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```
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### Faster R-CNN MobileNetV3-Large 320 FPN
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model fasterrcnn_mobilenet_v3_large_320_fpn --epochs 26\
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--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
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```
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### FCOS ResNet-50 FPN
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model fcos_resnet50_fpn --epochs 26\
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--lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01 --amp --weights-backbone ResNet50_Weights.IMAGENET1K_V1
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```
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### RetinaNet
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model retinanet_resnet50_fpn --epochs 26\
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--lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
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```
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### SSD300 VGG16
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model ssd300_vgg16 --epochs 120\
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--lr-steps 80 110 --aspect-ratio-group-factor 3 --lr 0.002 --batch-size 4\
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--weight-decay 0.0005 --data-augmentation ssd --weights-backbone VGG16_Weights.IMAGENET1K_FEATURES
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```
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### SSDlite320 MobileNetV3-Large
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model ssdlite320_mobilenet_v3_large --epochs 660\
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--aspect-ratio-group-factor 3 --lr-scheduler cosineannealinglr --lr 0.15 --batch-size 24\
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--weight-decay 0.00004 --data-augmentation ssdlite
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```
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### Mask R-CNN
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco --model maskrcnn_resnet50_fpn --epochs 26\
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--lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
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```
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### Keypoint R-CNN
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```
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torchrun --nproc_per_node=8 train.py\
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--dataset coco_kp --model keypointrcnn_resnet50_fpn --epochs 46\
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--lr-steps 36 43 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
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```
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