🦴💪 ScolioVis Training

This repository contains instructions for replicating our training process for our thesis project entitled: "ScolioVis: Automated Cobb Angle Measurement on Anterior-Posterior Spine X-Rays using Multi-Instance Keypoint Detection with Keypoint RCNN".

We also store the training scripts for evaluating the KeypointRCNN Model, as well as the instructions for replicating the training process for our research on Google Colab.

For more information on the whole project go to blankeos/scoliovis.

Instructions

1. Request Dataset 16: 609 spinal anterior-posterior x-ray images from SpineWeb

   You should receive a file called boostnet_labeldata.zip from them. Unzip that and save the root of the boostnet_labeldata folder on your Google Drive.

2. Execute and follow the steps on our Preprocessing Notebook

   The final data should look like:

   keypointsrcnn_data
   ├─images
   | ├─train
   | | ├─img_1.jpg
   | | └─(..).jpg
   | └─val
   |   ├─img.jpg
   |   └─(..).jpg
   └─labels
   | ├─img_1.json
   | └─(..).json
   └─val
       ├─img.json
       └─(..).json
   

   After following the instructions, your final data should be saved on your Google Drive for training later on.

3. Execute and follow the steps on our Training Notebook

4. You finished everything! 🎉

References:

• Training Scripts:

  • The training scripts in this repository can be obtained from either pytorch/vision/references/detection or alexppppp/keypoint_rcnn_training_pytorch.
  • I then changed coco_eval.py to contain the following since the model has 4 points for each instance detected:

    # some code here

• Detailed Medium Article on Custom Keypoint RCNN Training

pytorch/vision/references/detection content below ⬇

⚠⚠⚠ Everything below this point is from pytorch/vision/references/detection⚠⚠⚠

---

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 --weights-backbone ResNet50_Weights.IMAGENET1K_V1

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 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1

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 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1

FCOS ResNet-50 FPN

torchrun --nproc_per_node=8 train.py\
    --dataset coco --model fcos_resnet50_fpn --epochs 26\
    --lr-steps 16 22 --aspect-ratio-group-factor 3  --lr 0.01 --amp --weights-backbone ResNet50_Weights.IMAGENET1K_V1

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 --weights-backbone ResNet50_Weights.IMAGENET1K_V1

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 --weights-backbone VGG16_Weights.IMAGENET1K_FEATURES

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 --weights-backbone ResNet50_Weights.IMAGENET1K_V1

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 --weights-backbone ResNet50_Weights.IMAGENET1K_V1