Animal/Plant Species detection using CNN

Check this link for the task description: link

Team Members : Mohammed Safi Ur Rahman Khan, Vamsi Sai Krishna Malineni

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General Instructions:

1. Install the required libraries using the following command :

pip install -r requirements.txt

2. The project is divided into two parts: PART A and PART B. You can find the juypter notebooks in the respective folders. Along with jupyter notebooks we have also given python code filed (.py) files. These contain the code to direclty train and test the CNN in a non interactive way.

3. For seeing the outputs and the various explanations, and how the code has developed, please check the jupyter notebooks (i.e., .ipynb files for both part A and part B). For training and testing the CNN model from command line, run the (.py) file by following the instructions given in below sections.

4. If you are running the jupyter notebooks on colab, the libraries from the requirements.txt file are preinstalled, with the exception of wandb. You can install wandb by using the following command :

!pip install wandb

5. The dataset for this project can be found at : Dataset Link

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PART A : Training from Scratch

Running the code

As mentioned earlier, there are two files in the folder of Part A. One is a jupyter notebook and the other is the python code file.
The jupyter notebook has the outputs still intact so that can be used for reference.
The python file has all the functions and the code used in the jupyter file (along with some additional code that can be used to run from the command line)

Running the python file from the terminal

The python file can be run from the terminal by passing the various command line arguments. Please make sure that the unzipped folder of the dataset is as it is present in the same directory as this python file
There are two modes of running this file

1. Running the hyperparameter sweeps using wandb

python assgn2.py --sweep yes

The code will now run in the sweep mode and will enable wandb integration and log all the data in wand. Make sure you have wandb installed if you want to run in this mode. Also, change the entity name and project name in the code before running in this mode

2. Running in normal mode

python assgn2.py --sweep no --batchNorm xxx --numFilters xxx --filterOrg xxx --dropout xxx --dataAugment xxx --numEpochs xxx --batchSize xxx --denseLayer xxx --learningRate xxx --kernelSize xxx --denseAct xxx --convAct xxx

Replace xxx in above with the appropriate parameter you want to train the model with
For example:

python assgn2.py --sweep no --batchNorm True --numFilters 32 --filterOrg 2 --dropout 0.4 --dataAugment False --numEpochs 10 --batchSize 128 --denseLayer 512 --learningRate 0.0001 --kernelSize 3 --denseAct relu --convAct relu

Description of various command line arguments

1. --sweep : Do you want to sweep or not: Enter 'yes' or 'no'. If this is 'yes' then below arguments are not required. Enter below arguments only if this is 'no'
   
2. --batchNorm : Batch Normalization: True or False
   
3. --numFilters : Number of Filters: integer value
   
4. --filterOrg : Filter Organization: float value
5. --dropout : Dropout: float value
6. --dataAugment : Data Augmentation: True or False
7. --numEpochs : Number of Epochs: integer value
8. --batchSize : Batch Size: integer value
9. --denseLayer : Dense Layer size: integer value
10. --learningRate : Learning Rate: float value
11. --kernelSize : Kernel Size: integer value
12. --denseAct : Dense Layer Activation function: string value
13. --convAct : Conv Layer Activation function: string value

Running the jupyter notebook

This can be run in a sequential manner. i.e., one cell at a time. This notebook also has the code for plotting the various images required for the proje.

General Functions Description

1. Getting Train Dataset

Dataset for training and validation is prepared using the following functions :

• Un-augmented Dataset:

get_data()

• Augmented Dataset:

get_augmented_data()

2. Building Model

Buidling a small CNN network with 5 convolution layers can be done by using the following method :

build_cnn(conv_activation , dense_activation, num_filters, conv_filter_size, pool_filter_size, batch_norm, dense_layer, dropout)

where :

• conv_activation : dtype="List" activation used for convolution layer
• dense_activation : dtype="String" acitvation used for densely connected layers
• num_filters : dtype="List" number of activation filters for each layer
• conv_filter_size : dtype="List" kernel sizes for convultion layers
• pool_filter_size : dtype="List" kernel sizes for maxpooling layers
• batch_norm : dtype="Boolean" set to True, if you are using batch normalization
• dim_final : dtype="Integer" dimensionality of output space after 5 blocks of convultion, maxpooling blocks
• dropout : dtype="float or double" specify the dropout % for regularization (in decimals)

3. Hyperparameter Sweeps

The hyperparamter sweeps can be run using the following method

sweeper(entity_name, project_name)

where

• entity_name : Enter the wandb entity name
• project_name : Enter the wandb project name

The various hyperparameters used are :

hyperparameters={
    'batch_norm':        {'values':[True, False]},
    'num_filter':        {'values':[32, 64, 128, 256]},
    'filter_org':        {'values':[0.5, 1, 2]},
    'dropout':           {'values':[0.0, 0.5, 0.6, 0.4]},
    'data_augmentation': {'values':[True, False]},
    'num_epochs':        {'values':[10, 20, 30]},
    'batch_size':        {'values':[32, 64, 128]},
    'dense_layer':       {'values':[32, 64, 128, 512]},
    'learning_rate':     {'values':[0.001, 0.0001]},
    'kernel_size':       {'values': [3, 5, 7]}
    }   
sweep_config = {
      'method' : 'bayes','metric' :{'name': 'val_acc','goal': 'maximize'},
      'parameters': hyperparameters
    }

4. Training the model

The following function will define the model, train the model according to the hyperparameters given to it by wandb and logs the metrics to wandb.

 train()

Use the following function to run the wandb sweeps

sweeper(entity_name,project_name)

5. Getting Test Dataset

Use the following function to generate test dataset to determine the test accuracy of model with best performance in terms of validation accuracy.

get_test_data()

6. Testing the best model

Use the following function to determine the test accuracy of the best performing model, and log the metrics to wandb

testing(entity_name,project_name)

- The best trained model can be accessed at: https://drive.google.com/file/d/1aInmPFMV_rpJI_xPDP45h7XD4sGi1KaC/view?usp=sharing

The ipynb file contains all the necessary plots and the code to get them. The plots include

1. Plotting images with their true and predicted labels
   
2. Plotting the filters and the feature maps for a random image
   
3. Visualizing 10 random neurons using guided backpropagation

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PART B : Fine tuning a Pre-trained model

Running the code

As mentioned earlier, there are two files in the folder of Part B. One is a jupyter notebook and the other is the python code file.
The jupyter notebook has the outputs still intact so that can be used for reference.
The python file has all the functions and the code used in the jupyter file (along with some additional code that can be used to run from the command line)

Running the python file from the terminal

The python file can be run from the terminal by passing the various command line arguments. Please make sure that the unzipped folder of the dataset is as it is present in the same directory as this python file
There are two modes of running this file

1. Running the hyperparameter sweeps using wandb

python assgn2B.py --sweep yes

The code will now run in the sweep mode and will enable wandb integration and log all the data in wandb. Make sure you have wandb installed if you want to run in this mode. Also, change the entity name and project name in the code before running in this mode

2. Running in normal mode

python assgn2B.py --sweep no --model xxx --dropout xxx --dataAugment xxx --numEpochs xxx --batchSize xxx --denseLayer xxx --learningRate xxx --trainLayers xxx --denseAct xxx

Replace xxx in above with the appropriate parameter you want to train the model with
For example:

python assgn2B.py --sweep no --model Xception --dropout 0.3 --dataAugment False --numEpochs 10 --batchSize 32 --denseLayer 128 --learningRate 0.001 --trainLayers 10 --denseAct relu

Description of various command line arguments

1. --sweep : Do you want to sweep or not: Enter 'yes' or 'no'. If this is 'yes' then below arguments are not required. Enter below arguments only if this is 'no'
   
2. --model : Pretrained model to use: string value
   
3. --dropout : Dropout: float value
4. --dataAugment : Data Augmentation: True or False
5. --numEpochs : Number of Epochs: integer value
6. --batchSize : Batch Size: integer value
7. --denseLayer : Dense Layer size: integer value
8. --learningRate : Learning Rate: float value
9. --trainLayers : Number of trainable layers: integer value
10. --denseAct : Dense Layer Activation function: string value

Running the jupyter notebook

This can be run in a sequential manner. i.e., one cell at a time. This notebook also has the code for plotting the various images required for the project.

General Functions Description

1. Getting Train Dataset

Dataset for training and validation is prepared using the following functions :

• Un-augmented Dataset:

get_data()

• Augmented Dataset:

get_augmented_data()

2. Building Model

The following function is used to build a model based on a pretrained model:

build_model(model_name, dense_activation, dense_layer, dropout, trainable_layers)

where

• model_name : Enter the name of a pretrained model ("String")
• dense_activation : Enter the name of the activation function for dense layer ("String")
• dense_layer : Enter the number of units in the dense layer ("Integer")
• dropout : Enter the percent of drop out in decimals ("Double/float")
• trainable_layers : Enter the number of layers to be tuned ("Integer")

The available models for pretraining are : * `ResNet50` * `InceptionV3` * `InceptionResNetV2` * `Xception`

3. Hyperparameter Sweeps

sweeper(entity_name, project_name)

where

• entity_name : Enter the wandb entity name
• project_name : Enter the wandb project name

The various hyperparameters used are :

hyperparameters = {
        "model_name":{'values': ["InceptionV3", "ResNet50", "InceptionResNetV2", "Xception"]},
        "data_augmentation": {"values": [True, False]},
        "dense_layer": {"values": [64, 128, 256, 512]},
        "dropout": {"values": [0.0, 0.1, 0.2, 0.3]},        
        "trainable_layers": {"values": [0, 10, 15, 20]},
        "batch_size": {"values": [64, 128]},
        "num_epochs":{"values": [5, 10, 15]}
        }
sweep_config = {
  "method": "bayes",
  "metric": {"name":"val_acc","goal": "maximize"}, 
  "parameters": hyperparameters
   }

4. Training the model

The following function will define the model, train the model according to the hyperparameters given to it by wandb and logs the metrics to wandb.

 wandb_train()

Use the following function to run the wandb sweeps

sweeper(entity_name,project_name)

5. Getting Test Dataset

The test data can be accessed using the following function:

• Un-augmented data: Use this function if the best model for your data set returns that data augmentation is not necessary

get_test_data()

• Augmented data: Use this function if the best model for your data set returns that data augmentation is necessary

get_test_augmented_data()

6. Testing the best model

Use the following function to determine the test accuracy of the best performing model, and log the metrics to wandb

testing(entity_name,project_name)

RESULTS

The results and the learnings from this project can be found here: https://wandb.ai/safi-vamsi-cs6910/Assignment%202/reports/Assignment-2--VmlldzoxNzY2Njky