num_classes = 1 ## Number of classes, as this project is a binary classification task
## By default the value has been set to 1
## For multiclass, change the value according to number of classes
## Also, change the criterion accordingly in 'main.py' line no. 90
epochs = 20 ## Number of epochs
batch_size = 128 ## Size of minibatch
dataset_dir = 'smoke_data/dataset' ## put the root directory of the dataset here.
## This directory should contain two subfolders.
## One for smoke images and the other for non smoke images.
resize = True ## By deafult the images will be resized. If it's false,
## then the model will be trained on default size of the images
## and the dataset should not contain images of inconsistent size
num_workers = 4
image_size = (224, 224) ## Size of the target image after resizing,
## No color channel info is required.
pin_memory = True ## If the model is training on a cuda enabled device,
## then making this True will make the process faster
device = cuda/cpu ## No need to change
model_name = "resnet18" ## For this project ResNet18/ResNet34/ResNet50 models were trained
## There is no difference in inference, but ResNet18 is faster
## It gives 12++ fps on CPU
## Any model available in torchvision.models can be trained by changing this parameter.
model_path = "path/to/model" ## the model is saved according to the name and image size
## In case of some thing else, update it accordingly
## by default it saves the model in 'saved' folder.
learning_rate = 0.001 ## 0.001 is the default value of learning rate. Till now,
## This seems to be the optimal value. Higher values causes fluctuation
## and lower values causes slower convergence
## Anything between 0.001~0.005 is good enough
classification_threshold = 0.75 ## The output of sigmoid function is either
## <0.1 or >0.9 so the threshold value can be
## chosen anything between 0.4~0.8.
## But choosing a higher value reduces false positives
## Which can be seen in exceptional cases
There are three different transform functions for training, validation and testing. Training process requires heavy image augmentation. Otherwise, there is a tendency of the model to overfit and learn nothing. For the augmentation purpose, albumentations library has been used. The functions of the training transform are.
- CLAHE ## Contrast Limited Adaptive Histogram Equalization
## Used to balance any distortion in the lighting
- Cutout ## Creates small squares in the image and fills them with greyish color
## Significantly increases the accuracy as the model has
## a tendecy to identify white garbage as smoke
- Flip ## Flips the images randomly either horizontally or vertically
- RGBShift ## Randomly shift the value of RGB layers
- RandomFog ## Adds a fogginess in the images
## Improves the generalization of the model
- RandomBrightnessContrast ## Randomly changes the brightness/contrast of the images
All of the transform functions have two things in common.
- Normalization ## Normalizes the images
- ToTensorV2 ## COnverts to tensor
Once the config.py has been updated, the model can be trained by running
python main.py ## No extra argument is required
or,
python3 main.py
given that you are already in the project folder.
