VLFM-Driven Efficient Recognition of Surgical Incisions
Environment
This repo requires Pytorch>=1.9 and torchvision. We recommand using docker to setup the environment. You can use this pre-built docker image docker pull pengchuanzhang/maskrcnn:ubuntu18-py3.7-cuda10.2-pytorch1.9 or this one docker pull pengchuanzhang/pytorch:ubuntu20.04_torch1.9-cuda11.3-nccl2.9.9 depending on your GPU.
Then install the following packages:
pip install einops shapely timm yacs tensorboardX ftfy prettytable pymongo
pip install transformers
python setup.py build develop --user
Backbone Checkpoints. Download the ImageNet pre-trained backbone checkpoints into the MODEL folder.
mkdir MODEL
wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/swin_tiny_patch4_window7_224.pth -O swin_tiny_patch4_window7_224.pth
wget https://penzhanwu2bbs.blob.core.windows.net/data/GLIPv1_Open/models/glip_tiny_model_o365_goldg_cc_sbu.pth
Command. train see
bash surgin_train.sh
val see
bash test.sh
metric
python metric.py
emsemble see
python metric_em.py
based on: GLIP: Grounded Language-Image Pre-training