/smartphone_microscopy

AI-Assisted Smartphone Microscopy for automated detection of Giardia and Cryptosporidium cysts.

Primary LanguageJupyter Notebook

smartphone_microscopy

AI-Assisted Smartphone Microscopy for automated detection of Giardia and Cryptosporidium cysts.

This repository contains all the configurations and utilities used to train the models for AI Assisted Smartphone Microscopy.

Please find the dataset here.

mmdetection for Faster RCNN and RetinaNet

Installing dependencies

  1. Create a virtual environment.

    python3 -m venv .venv
source .venv/bin/activate

  2. Install torch==1.13.0.

    pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 --extra-index-url https://download.pytorch.org/whl/cu117

  3. Install mmcv==1.7.1.

    pip install mmcv-full==1.7.1 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html

  4. This repo uses mmdetection v2.27.0. Clone the mmdetection repo at v2.27.0 and install.

    git clone --depth 1 --branch v2.27.0 https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip install -v -e .

Model Training and Prediction

The config file used for training and testing are inside configs/faster_rcnn and configs/retinanet.

Training

python mmdetection/tools/train.py <config_file>

Prediction

python mmdetection/tools/test.py <config_file> \
        --gpu-id 0 \
        <checkpoint-path-for-fold> \
        --format-only \
        --options \
        "jsonfile_prefix=<output_folder>/<fold>/results_test"

The results are saved inside <output_folder>/<fold>/ folder named results_test.bbox.json. The code to analyze the results are present inside notebooks/result_analysis.ipynb notebook.

Yolov8

Installing dependencies

Download Yolov8s model weights from here.

pip install ultralytics

Change the fold number inside respective data and model files in configs/yolov8/data and configs/yolov8/model folders.

Training and prediction

Run the respective file inside configs/yolov8/model folder.

python configs/yolov8/model/brightfield_reference.py

The training and validation info will be saved inside runs folder.

Refer to https://github.com/ultralytics/ultralytics to further costumize yolov8 runs.