Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection

If you use the Tryp dataset, please cite the following:Esla Timothy Anzaku, Mohammed Aliy Mohammed, Utku Ozbulak, Jongbum Won, Hyesoo Hong, Janarthanan Krishnamoorthy, Sofie Van Hoecke, Stefan Magez, Arnout Van Messem, Wesley De Neve "Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection." Scientific Data 10, 716 (2023). https://doi.org/10.1038/s41597-023-02608-yThe <b>Tryp dataset</b> provides bounding box annotations for detecting<i> Trypanosoma brucei brucei</i> in microscopy images of unstained thick blood smears. Extracting the <b>Tryp.zip</b> file unveils three main folders:positive_imagesnegative_imagesvideosThe<b> videos</b> folder holds all the originally recorded videos, which were used to extract the images in the Tryp dataset and are categorized into <b>positive</b> and <b>negative</b> folders.Inside the <b>positive_images</b> folder are three more folders:trainvalidationtestEach folder contains two more folders,<b> images</b> and <b>labels</b>, and a JSON file. The <b>images</b> and <b>labels</b> folders hold the corresponding images and annotation files compatible with the YOLOv7 model. On the other hand, the JSON files have annotations in the MS COCO format, which is suitable for training the Faster R-CNN and RetinaNet models using the implementation by Torchvision.The related code is available at https://github.com/esla/trypanosome_parasite_detection<br>