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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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- detection |
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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 yolo_beetle_detection |
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This model detects beetles and scale bars in images by drawing bounding boxes around the respective items. This model was developed to facilitate downstream applications during BeetlePalooza 2024. |
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## Model Details |
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yolo_beetle_best.pt is the weights file for the YOLO model. |
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The yolov8m checkpoint was fine-tuned over 100 epochs on 29 annotated images of beetles sourced from the (2018-NEON-beetles dataset)[https://huggingface.co/datasets/imageomics/2018-NEON-beetles]. |
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Please checkout the repository on HF and cite information accordingly. |
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All 29 images were used as the training set. |
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## Model Description |
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The model is responsible for taking an input image (RGB) and generating bounding boxes for all classes below that are found in the image. Data augmentations applied during training include shear (10.0), scale (0.5), translate (0.1), fliplr (0.2), and flipud(0.2). |
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The model was trained for 100 epochs with a default image size of 640. |
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### Segmentation Classes |
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[`box class`] corresponding category |
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- [0] beetle |
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- [1] scale_bar |
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### Details |
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``` |
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model.train(data=YAML, |
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epochs=100, |
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batch=4, |
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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 = 0.2, |
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flipud = 0.2 |
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) |
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``` |
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## Metrics (Training) |
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Class Images Instances Box(P R mAP50 mAP50-95) |
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all 29 479 0.992 0.998 0.995 0.743 |
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beetle 29 450 0.991 0.997 0.995 0.714 |
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scale_bar 29 29 0.992 1 0.995 0.771 |
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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 [the 2018-NEON-beetles-processing github page](https://github.com/Imageomics/2018-NEON-beetles-processing/tree/beetle_detection) |
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<!-- ## Citation |
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**BibTeX:** |
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**APA:** |
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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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