DeepParticle dataset [MICRO, MESO, MACRO] (2022)

These are the datasets used to train the models described in our method paper:
"Computer vision segmentation model—deep learning for categorizing microplastic debris"
The image segmentation and classification models are designed to identify plastic particles from an image. Two types of models were built:
Segmentation models which segment and label the particle found within an input image with a homogeneous backgroundClassification models that classify a particle image into a category (e.g. hard plastic, pellet, line, cap...)See our Gitlab page: https://gitlab.com/Grouumf/particle_detect
Each folder: MICRO, MESO, and MACRO represents a different size category.
MICRO: 0.05 cm–0.5 cmMESO: 0.5 cm–5.0 cmMACRO > 5.0 cmThe VALIDATION dataset contains additional annotated images used for validation.
Each folder is organized as follows:
MICRO
├── annotation
├── foam
├── hard
├── line
├── noise
├── pellet
├── raw_img
└── reference

`raw_img` contains the raw images that each display a variety of plastic particles.`annotation` contains the particle pixel coordinates within the raw images.The remaining folders represent particle categories (line, noise, pellet, foam, reference). Each of these folders contains the individual image of each particle annotated in the raw imagesFor example, the MICRO annotation folder contains a TSV file named `08_03_micro_line_only1.JPG.tsv`. This file contains the particle annotations and coordinates for the raw images labeled: `08_03_micro_line_only1.JPG`
The beginning of the TSV file is as follows:
ID minr minc maxr maxc class
line/2022-03-28 07:18:39.577051_k0.jpg 2255 530 2329 601 line
line/2022-03-28 07:18:39.577051_k1.jpg 2185 754 2363 842 line
line/2022-03-28 07:18:39.577051_k2.jpg 2974 863 3079 921 line
Each line represents an annotated particle, with the four first columns its pixel coordinates. the last column is the annotated category.
Also, the image of the first annotated particle `line/2022-03-28 07:18:39.577051_k0.jpg` can be found in the `line` folder.
The MESO and MACRO folders contain different categories, e.g. cap_me, or butt_me, which are the bottle caps and the cigarette butt used for the MESO models.
Finally, the noise folders contain noise "particles" or sections of the raw images that should not be identified as particles. These are useful when training the classifiers and removing the false positives from the annotations.