yacwc

old_files/README.md

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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
+```
+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
+```
+
+### SSD300 VGG16
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env 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
+```
+python -m torch.distributed.launch --nproc_per_node=8 --use_env 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
+```
+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
+```
+