Maritime Detection, Classification and tracking data set and can be downloaded from http://www.dis.uniroma1.it/~labrococo/MAR/classification.htm The training set contains images from 24 different categories of boats navigating in the City of Venice (Italy). The .rar file contains a folder for each category. The jpeg files inside the folders are named according to the date, hour, and system track number. The folder "Water" contains false positives

1.Tensorflow
https://www.tensorflow.org/install/install_linux

2.Pandas
pip install pandas

3.Numpy
pip install numpy

4.clone this repo
git clone [email protected]:adiltirur1/Image_classification.git

Copy the training and testing data set in to
/image_classifier/supporting_files/data_set

from the root directory of this repository run the command


python -m code.retrain --bottleneck_dir=supporting_files/bottlenecks --how_many_training_steps=500 --model_dir=supporting_files/models/ --summaries_dir=supporting_files/training_summaries/"${ARCHITECTURE}" --output_graph=supporting_files/retrained_graph.pb --output_labels=supporting_files/retrained_labels.txt --inception_v3 --image_dir=supporting_files/sc5

from the root directory again run the following command from the root directory again run the following command


python -m code.bulk_classify --graph=supporting_files/retrained_graph.pb

navigate to
image_classification/code

Run the command


python accuracy.py