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Training model and Evaluation

Inception model evaluation.ipynb IPython notebook for evaluating inception model

Training Classifier.ipynb IPython notebook for training xgboost classifier

testing.csv testing dataset

train_test.csvtraining and testing dataset

training.csv training dataset

xgb.model classification model

Results

  • Example of classification results
Image name 1st result 1st score 2nd result 2nd score 3rd result 3rd score 4th result 4th score 5th result 5th score
15.38203756895384-100.1636399994293-120-2013-09-5-Tree lakeside, lakeshore 0.2218 worm fence 0.110338 swing 0.0501 golf ball 0.0215 golfcart, golf cart 0.0179

Evaluation of Inception Model (Image Recognition)

  • Performance of inception model (All classes)
named Top-5 Top-1 Top-1 & thresh=0.15 Top-1 & thresh=0.30
sample size 60920 13274 7456 3739
accuracy 0.2774 0.3837 0.5053648 0.6060
  • Performance of inception model (Each class)
named flowerpot stupa, tope water jug water bottle trash can greenhouse milk can barrel, cask canoe rain barrel lakeside Dutch oven
Top-5_size 11589 232 648 208 5587 9850 3017 1071 1737 2008 25291 496
Top-5_acc 0.4220 0.2759 0.4012 0.4087 0.2980 0.3637 0.4475 0.5770 0.3172 0.5364 0.2355 0.4798
Top-1_size 4236 33 48 40 925 1552 470 109 182 319 5292 68
Top-1_acc 0.4498 0.48485 0.4583 0.475 0.4497 0.3937 0.5766 0.5596 0.5879 0.6677 0.2674 0.5441
Thresh_size 2862 17 21 19 476 961 229 67 95 206 2471 32
Thresh_acc 0.5346 0.7647 0.7143 0.5263 0.6492 0.4412 0.8165 0.7015 0.6526 0.7621 0.4027 0.5938

Evaluation of Machine Learning Classifier

Classification based on Top-5 results

  • Training and Testing set
Sample sizes Yes No Ratio
Dataset 60920 16902 44018 0.28
Training set 50920 11567 38723 0.23
Testing set 10000 5000 5000 0.50
  • Classification Performance of XGBoost
Training Testing
Accuracy 0.7666 0.5025

Classification based on Top-1 result

Sample sizes
Dataset 60920
Select where top-1 score > 0.15 32658
Select where top-1 = breeding sites 7456
  • Training and Testing set
Sample sizes Yes No Ratio
Training set 5219 2657 2,562 0.5091
Testing set 2237 1111 1126 0.4966
  • Classification Performance
Individual classifier Voting classifier Final Classifier
SVM Decision Tree Knn Random Forest SVM + KNN + Decision Tree Logistic Regression + Random Forest + GaussianNB XGBoost
0.5739 0.5928 0.5928 0.6366 0.6165 0.6477 0.6822