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General info

This is the repository to be used for NIBIO deparment tutorial.

Dataset

The original dataset is from here.

The local NIBIO copy of the dataset is here

Austrian Tree Dataset Overview

Collected by: Austrian Federal Forests AG
Collection Period: Autumn 2009 - Spring 2010
Usage: Non-commercial research only

Publication Reference

Fiel, S. & Sablatnig, R. (2010): Leaf classification using local features. In: Proc. of 34th annual Workshop of the Austrian Association for Pattern Recognition (AAPR), 2010, 69-74 pdf.

Dataset Contents

1. Leaves of Broad Leaf Trees

  • Total Images: 134
  • Types:
    • Ash (25 images)
    • Beech (30 images)
    • Hornbeam (34 images)
    • Mountain Oak (22 images)
    • Sycamore Maple (23 images)
  • Details:
    • Image Scale: 800 pixels height or 600 pixels width
    • Note: Ash leaves are compound, specifically pinnate

2. Bark of Trees

  • Total Images: 1183
  • Types:
    • Ash (34 images)
    • Beech (16 images)
    • Black Pine (166 images, divided into 3 age-based sub-classes)
    • Fir (127 images, divided into 3 age-based sub-classes)
    • Hornbeam (42 images)
    • Larch (200 images, divided into 3 age-based sub-classes)
    • Mountain Oak (77 images)
    • Scots Pine (190 images, divided into 3 age-based sub-classes)
    • Spruce (213 images, divided into 3 age-based sub-classes)
    • Swiss Stone Pine (96 images)
    • Sycamore Maple (22 images)
  • Details:
    • Image Scale: 800 pixels height or 600 pixels width
    • Age Categories:
      • Less than 60 years
      • 60 to 80 years
      • More than 80 years

3. Needles of Conifers

  • Total Images: 275
  • Types:
    • Black Pine (107 images)
    • Fir (10 images)
    • Larch (114 images)
    • Scots Pine (10 images)
    • Spruce (13 images)
    • Swiss Stone Pine (21 images)
  • Details:
    • Needle Classes:
      • Separate growth (Fir, Spruce)
      • Cluster growth (others)
    • Lighting:
      • Perfect conditions (Fir, Scots Pine, Spruce)
      • Natural conditions (others)

Getting started with the tutorial

Use Google Colab and create a new project.

Clone the repo in the google collab using the following command !git clone https://gitlab.nibio.no/maciekwielgosz/ml-department-workshop.git

Install the packet to be used for getting the data : !pip install gdown.

Get the data with the following command: !gdown https://drive.google.com/uc?id=1D6z3UbCoBOhOs8lhasgm-ap58-uPdDY-

The basis for the tutorial is the conrent of the run.py script in the folder of the cloned repository. You can gradulaly copy the commands from there, modify and run them.