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Temple Classification

The repository contains the prediction code for a classifier that could guess which country the temple is in. The code gets the path to a directory with images as a parameter, and returns a CSV file with the results. The solution has option of three pretrained models: Efficient Net B3, Efficient B0, VGG16. Efficient Net B3 is the default model since it gives the highest accuracy.

Setup

Requirements

library version
numpy 1.22.1
opencv-python 4.5.5.62
pandas 1.4.0
Pillow 9.0.0
python-dateutil 2.8.2
pytz 2021.3
six 1.16.0
torch 1.10.1
torchvision 0.11.2
typing_extensions 4.0.1

Steps for installing requirements

pip3 install -r requirements.txt

Usage

bash predict.sh --input_path [PATH TO IMAGE DIRECTORY] --model [MODEL NAME] --pretrained_path [Path of the Pretrained Model] --output_dir [Path to the directory where output will be written] --image_shape [Dimension of the input shape]

Arguments

Argument Required/Optional Meaning
--input_path Required Path of the Input Directory
--model Optional [Default: EFFB3] Prediction Model to be used
--pretrained_path Optional [Default: models/temple-classifier-eff-best.pt] Path of the pretrained model
--output_dir Optional [Default: data/output/] Path of the Output Directory
--image_shape Optional [Default: 300] Width and Height of the image

Examples

  • bash predict.sh --input_path data/input/
  • bash predict.sh --input_path data/input/ --model EFFB0 --pretrained_path models/temple-classifier-effb0.pt --image_shape 224
  • bash predict.sh --input_path data/input/ --model VGG16 --pretrained_path models/temple-classifier-vgg.pt --image_shape 224

Paste the pretrained models downloaded from the link below in the models file

Helper for the bash script

bash predict.sh --help

Training Process of prediction model

The images were divided in training and test dataset on a 80:20 split.

After Training-Testing split, the distribution is as follows

Category #Images
Train 576
Test 138

Data augmentation was performed on training data.

The following augmentation techniques were applied:

  • Random 90 Degree Rotation
  • Random Crop
  • Adding Gaussian Noise
  • Adding Fog like noise
  • Changing the color temperature(to give a night likeview)
  • Redacting Random Parts of image

Data Augmentation is applied such that each class is augmented till we reach to a max of 400 image per class or we have augmented each image with a factor of 10

Distribution of Each Class Before Augmentation

Class Count
Australia 28
Indonesia-Bali 36
Germany 86
Armenia 9
Portugal+Brazil 44
Japan 50
Thailand 84
Spain 55
Malaysia+Indonesia 44
Hungary+Slovakia+Croatia 40
Russia 100
Total 576

Distribution of Each Class After Augmentation

Class CountOfImagesAfterAugmentation
Australia 280
Indonesia-Bali 360
Germany 400
Armenia 90
Portugal+Brazil 400
Japan 400
Thailand 400
Spain 400
Malaysia+Indonesia 400
Hungary+Slovakia+Croatia 400
Russia 400
Total 3930

Each class of augmented train image is now randomly split into train and val dataset in 80:20

Final Distribution of Data

Category #Images
train 3144
val 786
test 138

Training the model

The model is now finetuned on various CNN architectures trained on Imagenet data, Following changes are made to each of the net

  • The size of the final layer is changed to suit the dataset making the size to x,11, x is the size of the output of the previous layer
  • The weights of all but last CNN layer is freezed during the training process

Results of Test Set

Model Accuracy Weights
EfficientNet B3 84.061 effb3weights
EfficientNet B0 81.88 effb0weights
VGG16 81.15 vggweights

Analysis of the Result

Efficientnet B3 Confusion Matrix

Efficientnet B0 Confusion Matrix

VGG16 Confusion Matrix

Classwise Accuracy of the three models

Please Look at notebook modelTraining.iypnb in the folder notebooks for more analysis performed; The notebook explains the reason for bottom two underperforming class

Armenia is not considered in tests due to only having 9 unaugmented images in train+val dataset

Notebooks

Following are the notebooks present in the notebooks folder

  • ImageClassification_Efficientnet_B3.ipynb: Contains the training code for Efficient Net B3
  • ImageClassification_Efficientnet_B0.ipynb: Contains the training code for Efficient Net B0
  • ImageClassification_Efficientnet_VGG.ipynb: Contains the training code for VGG16
  • modelTesting.ipynb: Contains the result calculation and the analysis of the results on test dataset
  • DataSplit-Augmentation.ipynb: Contains the code to split the code in train and test; augmenment the train; split the augmented data in train and val
  • DataVisualisation.ipynb: Visualiztion of data distribution

Dataset used

Category Datalink
train train
val val
test test