Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+tags:
+- biology
+- CV
+- images
+- animals
+- lepidoptera
+- butterflies
+- detection
+- heliconius
+- forewings
+- hindwings
+- separated wings
+- full body
+- butterfly
+- RGB
+- ruler
+- whitebalance
+- label
+- colorchecker
+---
+
+## Model Card for butterfly_detection_yolo
+
+This model takes in images of butterflies as photographed for museum collections and detects butterfly components (L/R forewings, L/R hindwings and body) as well as color checkers and metadata labels.
+The detection model described here is used in the repository https://github.com/Imageomics/wing-segmentation to detect components and use Meta's Segment-Anything (SAM) model for segmentation of components.
+
+## Model Details
+
+yolo_detection_8m_shear_10.0_scale_0.5_translate_0.1_fliplr_0.0_best.pt is the butterfly detection model.
+
+The yolo v8 detection model was trained on a dataset of 800 total images from the [Heliconius Collection-Cambridge Butterfly](imageomics/Heliconius-Collection_Cambridge-Butterfly), OM_STRI, and Monteiro datasets. The model uses the pretrained yolov8m.pt model. 
+
+## Model Description
+
+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), 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      Box(P          R      mAP50  mAP50-95)
+                   all         64        358      0.979      0.887      0.919      0.877
+            background         64          3          1          0      0.315      0.169
+        right_forewing         64         58      0.995      0.983      0.986      0.977
+         left_forewing         64         51      0.975          1      0.985      0.982
+        right_hindwing         64         59      0.997      0.966      0.993      0.977
+         left_hindwing         64         50      0.975          1      0.993       0.98
+                 ruler         64         31      0.951          1      0.995      0.952
+         white_balance         64         18      0.984          1      0.995      0.995
+                 label         64         50      0.996          1      0.995      0.935
+            color_card         64         24      0.988          1      0.995      0.992
+                  body         64         14      0.928      0.921      0.939      0.815
+                  
+**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](https://github.com/Imageomics/wing-segmentation)
+
+## 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](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.
+