license: mit
language:
- en
tags:
- biology
- CV
- images
- animals
- lepidoptera
- butterflies
- segmentation
- heliconius
- forewings
- hindwings
- separated wings
- full body
- butterfly
- RGB
- ruler
- whitebalance
- label
- colorchecker
Model Card for butterfly_segmentation_yolo_v8
This model takes in an image of a butterfly (with or without body attached to wings) and segments out any existing hindwings and forewings, in addition to pictured equipment described below.
Model Details
yolov8m_shear_10.0_scale_0.5_translate_0.1_fliplr_0.0_best.pt is the butterfly segmentation model.
The butterfly segmentation model was trained on a dataset of 800 total images from the Jiggins, OM_STRI, and Monteiro datasets. The model architecture is based on YOLO v8 (yolov8m-seg.pt), which we fine-tune further on our dataset of 800 images.
Model Description
The model is responsible for taking an input image (RGB) and generating segmentation masks for all classes below that are found in the image. Data augmentations applied during training include shear (10.0), scale (0.5), and translate (0.1). The model was trained for 50 epochs with an image size of 256. Note that despite defining an image size of 256, the normalized masks predicted by yolo can be rescaled to the original image size.
Segmentation Classes
[pixel class] corresponding category
- [0] background
- [1] right_forewing
- [2] left_forewing
- [3] right_hindwing
- [4] left_hindwing
- [5] ruler
- [6] white_balance
- [7] label
- [8] color_card
- [9] body
Details
model.train(data=YAML, imgsz=256, epochs=50, batch=16, device=DEVICE, optimizer='auto', verbose=True, val=True, shear=10.0, scale=0.5, translate=0.1, fliplr = 0.0 )
Metrics
Class Images Instances mAP50-95
all 64 358
background 64 3 0.20946
right_forewing 64 58 0.9845
left_forewing 64 51 0.9682
right_hindwing 64 59 0.95296
left_hindwing 64 50 0.93961
ruler 64 31 0.73608
white_balance 64 18 0.90686
label 64 50 0.80865
color_card 64 24 0.92653
body 64 14 0.78283
Developed by: Michelle Ramirez
How to Get Started with the Model
To view applications of how to load in the model file and predict masks on images, please refer to this github repository
Citation
BibTeX:
@software{Ramirez_Lepidoptera_Wing_Segmentation_2024,
author = {Ramirez, Michelle},
doi = {10.5281/zenodo.10869579},
month = mar,
title = {{Lepidoptera Wing Segmentation}},
url = {https://github.com/Imageomics/wing-segmentation},
version = {1.0.0},
year = {2024}
}
APA:
Ramirez, M. (2024). Lepidoptera Wing Segmentation (Version 1.0.0) [Computer software]. https://doi.org/10.5281/zenodo.10869579
Acknowledgements
The Imageomics Institute is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under Award #2118240 (Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.