PolypDiffDet-: A Python repository from haopd12

Getting Started with DiffusionDet

Introduction

This is the official code of DiffusionDet for Polyp Detection. We extend DiffusionDet for testing the result of using this new method instead of using YOLO...

Installation

The codebases are built on top of Detectron2, Sparse R-CNN, and denoising-diffusion-pytorch. Thanks very much.

Requirements

Linux or macOS with Python ≥ 3.6
PyTorch ≥ 1.9.0 and torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this
OpenCV is optional and needed by demo and visualization

Steps

Install Detectron2 following https://github.com/facebookresearch/detectron2/blob/main/INSTALL.md#installation.
Prepare datasets

Install PolypsSet from Harvard Dataverse following https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2FFCBUOR&fbclid=IwAR1qCNJWosoTP0Q9luPlY9vK2Ag-9MjmV-_JMpCFD91TRLIaD0UkuH--Aus
Unzip file and save in the following directory ./PolypsSet/*
To get dataset's instances, run following command:

cd PolypsSet
python devide_train_set.py
python devide_test_set.py
cd ..

Prepare pretrain models

DiffusionDet uses three backbones including ResNet-50, ResNet-101 and Swin-Base. The pretrained ResNet-50 model can be downloaded automatically by Detectron2. We also provide pretrained ResNet-101 and Swin-Base which are compatible with Detectron2. Please download them to DiffusionDet_ROOT/models/ before training:

mkdir models
cd models
# ResNet-101
wget https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/torchvision-R-101.pkl

# Swin-Base
wget https://github.com/ShoufaChen/DiffusionDet/releases/download/v0.1/swin_base_patch4_window7_224_22k.pkl

cd ..

Thanks for model conversion scripts of ResNet-101 and Swin-Base.

Train DiffusionDet

python train_net.py --num-gpus 1 \
    --config-file configs/diffdet.polyp.res50.yaml

Evaluate DiffusionDet

python train_net.py --num-gpus 8 \
    --config-file configs/diffdet.polyp.res50.yaml \
    --eval-only MODEL.WEIGHTS path/to/model.pth

Evaluate with 4 refinement steps by setting MODEL.DiffusionDet.SAMPLE_STEP 4.

Inference Demo with Pre-trained Models

We provide a command line tool to run a simple demo of a set of images following Detectron2.