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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- biology |
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- CV |
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- images |
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- animals |
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- lepidoptera |
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- butterflies |
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- segmentation |
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- heliconius |
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- forewings |
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- hindwings |
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- separated wings |
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- full body |
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- butterfly |
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- RGB |
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- ruler |
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- whitebalance |
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- label |
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- colorchecker |
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--- |
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## Model Card for butterfly_segmentation_yolo_v8 |
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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. |
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## Model Details |
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yolov8m_shear_10.0_scale_0.5_translate_0.1_fliplr_0.0_best.pt is the butterfly segmentation model. |
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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. |
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## Model Description |
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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. |
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### Segmentation Classes |
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[`pixel class`] corresponding category |
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- [0] background |
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- [1] right_forewing |
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- [2] left_forewing |
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- [3] right_hindwing |
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- [4] left_hindwing |
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- [5] ruler |
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- [6] white_balance |
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- [7] label |
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- [8] color_card |
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- [9] body |
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### Details |
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model.train(data=YAML, |
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imgsz=256, |
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epochs=50, |
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batch=16, |
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device=DEVICE, |
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optimizer='auto', |
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verbose=True, |
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val=True, |
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shear=10.0, |
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scale=0.5, |
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translate=0.1, |
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fliplr = 0.0 |
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) |
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## Metrics |
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Class Images Instances mAP50-95 |
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all 64 358 |
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background 64 3 0.20946 |
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right_forewing 64 58 0.9845 |
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left_forewing 64 51 0.9682 |
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right_hindwing 64 59 0.95296 |
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left_hindwing 64 50 0.93961 |
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ruler 64 31 0.73608 |
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white_balance 64 18 0.90686 |
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label 64 50 0.80865 |
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color_card 64 24 0.92653 |
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body 64 14 0.78283 |
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**Developed by:** Michelle Ramirez |
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## How to Get Started with the Model |
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To view applications of how to load in the model file and predict masks on images, please refer to [this github repository](https://github.com/Imageomics/wing-segmentation) |
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## Citation |
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**BibTeX:** |
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``` |
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@software{Ramirez_Lepidoptera_Wing_Segmentation_2024, |
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author = {Ramirez, Michelle}, |
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doi = {10.5281/zenodo.10869579}, |
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month = mar, |
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title = {{Lepidoptera Wing Segmentation}}, |
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url = {https://github.com/Imageomics/wing-segmentation}, |
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version = {1.0.0}, |
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year = {2024} |
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} |
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``` |
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**APA:** |
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Ramirez, M. (2024). Lepidoptera Wing Segmentation (Version 1.0.0) [Computer software]. https://doi.org/10.5281/zenodo.10869579 |
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## Acknowledgements |
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The [Imageomics Institute](https://imageomics.org) is funded by the US National Science Foundation's Harnessing the Data Revolution (HDR) program under [Award #2118240](https://www.nsf.gov/awardsearch/showAward?AWD_ID=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. |
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