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Detection of Animals in camera trapped images using RetinaNet in pytorch.

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Animal-Detection

Link for saved weights

  • Commands for evaluating the model on heldout test set.
mkdir Test_Images

All the images will be placed in this folder

mkdir Test_Images_Final 

This folder will contain all the images after data cleaning and converting the annotations.json to annotations_test.csv.

Keep the annotations.json file in the same directory as the clean_data.py.

  • For testing, first clean the test data:
python clean_data.py
  • Command for testing Baseline Model:
python test_baseline.py --test_anno_file annotations_test.csv --type baseline
  • Command for testing the Baseline Model with Ensemble Approach:
python test_baseline.py --test_anno_file annotations_test.csv --type baseline_ensemble
  • Command for testing the Baseline Model with Weighted Random Sampling:
python test_improved.py --test_anno_file annotations_test.csv --type improved
  • Command for testing the Baseline Model with Weighted Random Sampling and Ensemble Approach:
python test_improved.py --test_anno_file annotations_test.csv --type improved_ensemble
  • Command for visualising the Baseline Model:
python visualize.py --dataset csv --csv_classes classname2id.csv --csv_val annotations_test.csv --model <saved_model_pth>
  • Command for visualising results with Weighted Random Sampling:
python visualize.py --dataset csv --csv_classes classname2id.csv --csv_val annotations_test.csv --model <saved_model_pth>
  • Command for visualising results with Ensemble Approach:
python visualize.py --dataset csv --csv_classes classname2id.csv --csv_val annotatoins_test.csv --model <saved_model_pth>

Team

  • Anish Madan
  • Sarthak Bhagat
  • Shagun Uppal