Explanations.txt

# Instructions
------------

* Create a directory tree structure as follows:

environment
|_ code
	|_ evaluate.py
	|_ train.py
|_ datasets
	|_dataset102	#The dataset to train on
		|_species1	#Each folder should contain a single species, with the folder name being the species name
			|_ ...  #Specimen pictures
		|_species2
		...
|_ labels 			#It will store the species names
|_ models 			#It will store the output model of train.py, used to classify using evaluate.py
|_ test_sets		#It will store the testing dataset
|_ tmp				#It will store the original untrained model as well as the "bottlenecks" (i. e. resized, compressed pictures)
|_ run.py

* To reproduce the study: modify the dataset name (102, 202 or 302) in file run.py and simply run the script:

    cd environment
    python train.py

* To train the CNN on a new dataset with new parameters, run the following in the terminal (with different parameters if needed):

	python -m code.train --validation_percentage=10 \
	--test_seed=1 \
	--bottleneck_dir=tmp/bottlenecks \
	--test_set=test_sets/example/ \
	--model_dir=tmp/models/ \
	--summaries_dir=tmp/training_summaries/ \
	--output_graph=models/example.pb \
	--output_labels=labels/example.txt \
	--architecture="mobilenet_1.0_224" \
	--image_dir=datasets/example_dataset \
	--print_misclassified_test_images \
	--how_many_training_steps=4000 \
	--learning_rate=0.01

* To classify a series of new pictures with the trained model, run a python script containing the following (with folder and file names changed accordingly):

    import code.evaluate
    model_file = "example.pb"
    dataset = "new_dataset_directory"
    label_file = "labels.txt"
    evaluate.evaluateDirectory(model_file, dataset, label_file, top_n=5, save_file="results.txt")