maskrcnn-deadwood / README.md
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metadata
license: apache-2.0
tags:
  - UAV
  - Deadwood
  - detectron2

Model description

This model is trained for detecting both standing and fallen deadwood from UAV RGB images. More thorough description is available on https://mayrajeo.github.io/maskrcnn-deadwood.

Training data

The model was trained on expert-annotated deadwood data, acquired on during leaf-on season 16.-17.7.2019 from Hiidenportti, Sotkamo, Eastern-Finland. The ground resolution for the data varied between 3.9 and 4.4cm. In addition, the model was tested with data collected from Evo, Hämeenlinna, Southern-Finland, acquired on 11.7.2018. The data from Evo was used only for testing the models.

Metrics

Metric Hiidenportti Evo
Patch AP50 0.704 0.519
Patch AP 0.366 0.252
Patch AP groundwood 0.326 0.183
Patch AP uprightwood 0.406 0.321
Scene AP50 0.683 0.511
Scene AP 0.341 0.236
Scene AP groundwood 0.246 0.160
Scene AP uprightwood 0.436 0.311

How to use

from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.data import build_detection_test_loader
import cv2

cfg = get_cfg()
cfg.merge_from_file(<path_to_model_config>)
cfg.OUTPUT_DIR = '<path_to_output>'
cfg.MODEL.WEIGHTS = '<path_to_weights>'
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # score threshold for detections
predictor = DefaultPredictor(cfg)

img = cv2.imread('<path_to_image_patch>')
outputs = predictor(image)