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 yolo_beetle_detection
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.
Model Details
yolo_beetle_best.pt is the weights file for the YOLO model. 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]. Please checkout the repository on HF and cite information accordingly.
All 29 images were used as the training set.
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), translate (0.1), fliplr (0.2), and flipud(0.2). The model was trained for 100 epochs with a default image size of 640.
Segmentation Classes
[box class] corresponding category
- [0] beetle
- [1] scale_bar
Details
model.train(data=YAML,
epochs=100,
batch=4,
device=DEVICE,
optimizer='auto',
verbose=True,
val=True,
shear=10.0,
scale=0.5,
translate=0.1,
fliplr = 0.2,
flipud = 0.2
)
Metrics (Training)
Class Images Instances Box(P R mAP50 mAP50-95)
all 29 479 0.992 0.998 0.995 0.743
beetle 29 450 0.991 0.997 0.995 0.714
scale_bar 29 29 0.992 1 0.995 0.771
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 the 2018-NEON-beetles-processing github page