https://github.com/blankeos/scoliovis-training
📝 Documentation and Training Scripts for ScolioVis
https://github.com/blankeos/scoliovis-training
Last synced: 4 months ago
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📝 Documentation and Training Scripts for ScolioVis
- Host: GitHub
- URL: https://github.com/blankeos/scoliovis-training
- Owner: Blankeos
- Created: 2022-11-15T17:31:02.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-03-24T16:31:21.000Z (over 3 years ago)
- Last Synced: 2025-08-11T19:44:10.616Z (11 months ago)
- Language: Python
- Size: 20.4 MB
- Stars: 5
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🦴💪 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](https://github.com/Blankeos/scoliovis).
### Instructions
1. Request [Dataset 16: 609 spinal anterior-posterior x-ray images](http://spineweb.digitalimaginggroup.ca/Index.php?n=Main.Datasets#Dataset_16.3A_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](https://colab.research.google.com/drive/1Rlt43PWo6NYREuDsGT8K5tRg5QqfFdVc?usp=sharing)**
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](https://colab.research.google.com/drive/1aaTWt2rZ-M7YlqIus7aC-84SorjNwl8G?usp=sharing)**
4. You finished everything! 🎉
### References:
- Training Scripts:
- The training scripts in this repository can be obtained from either [**pytorch**/vision/references/detection](https://github.com/pytorch/vision/tree/main/references/detection) or [**alexppppp**/keypoint_rcnn_training_pytorch](https://github.com/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:
```py
# some code here
```
- [Detailed Medium Article on Custom Keypoint RCNN Training](https://medium.com/@alexppppp/how-to-train-a-custom-keypoint-detection-model-with-pytorch-d9af90e111da)
> **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=`
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
```