Simple CNN

`Simple CNN` is a pipeline which can be use to train and infer CNN models by use of PyTorch and ONNX. It's simple and easy to USE !!! 🔥🔥

Install

Clone the repo and install requirements.txt in a Python environment

    git clone https://github.com/LahiRumesh/simple_cnn.git
    cd simple_cnn
    pip install -r requirements.txt

Data Preparation

Split images into train and val folders with each class the Image Folder 📂.. i.e for cat vs dogs classification, there should be a cat folder and dog folder in both train and val. The following folder structure illustrates 3 classes

├── Image_Folder
     ├── train
     │   │───── class1
     │   │     ├── class1.0.jpg
     │   │     ├── class1.1.jpg
     │   │     ├── class1.2.jpg
     │   │     ├── .........
     │   │     └── class1.500.jpg
     │   │
     │   │───── class2
     │   │     ├── class2.0.jpg
     │   │     ├── class2.1.jpg
     │   │     ├── class2.2.jpg
     │   │     ├── .........
     │   │     └── class2.500.jpg
     │   │
     │   └───── class3
     │          ├── class3.0.jpg
     │          ├── class3.1.jpg
     │          ├── class3.2.jpg
     │          ├── .........
     │          └── class3.500.jpg   
     │
     └── val
         │───── class1
         │     ├── class1.501.jpg
         │     ├── class1.502.jpg
         │     ├── class1.503.jpg
         │     ├── .........
         │     └── class1.600.jpg
         │
         │───── class2
         │     ├── class2.501.jpg
         │     ├── class2.502.jpg
         │     ├── class2.503.jpg
         │     ├── .........
         │     └── class2.600.jpg
         │
         └───── class3
               ├── class3.501.jpg
               ├── class3.502.jpg
               ├── class3.503.jpg
               ├── .........
               └── class3.600.jpg

Training

After the data preparation, it's time for the training !

Use the config.py to set the parameters, here are few parameters.

   cfg.data_dir = 'Data/Images/Image_Folder' # Image Folder path which contain train and val folders 
   cfg.device = '0' # cuda device, i.e. 0 or 0,1,2,3    
   cfg.image_size = 224 #input image size
   cfg.batch_size = 8 # batch size
   cfg.epochs = 50 #number of epochs

   cfg.model = 'resnet18' # torch vision classification model architectures for image classification 
                          # i.e. resnet18 or vgg16, alexnet, densenet121, squeezenet1_0

   cfg.pretrained = True  # use pretrained weights for training

   #Early Stopping
   cfg.use_early_stopping = True # use Early stopping
   cfg.patience = 8 # how many epochs to wait before stopping when accuracy is not improving
   cfg.min_delta = 0.0001 # minimum difference between new accuracy and old accuracy for new accuracy to be considered as an improvement

Here are the Available pre-trained models in Simple CNN

Architectures	Available Models
Resnet	resnet18, resnet34, resnet50, resnet101, resnet152
VGG	vgg13, vgg13_bn, vgg16, vgg16_bn, vgg19, vgg19_bn
Densenet	densenet121, densenet169, densenet161 , densenet201
Squeezenet	squeezenet1_0, squeezenet1_1
Alexnet	alexnet

Run cnn_train.py to start the training, all the logs will save in wandb, and ONNX weight files will save in the "models/Image_Folder" folder for each training experiment with the model name.

Inference

After the training process, use the exported ONNX model for inference using cnn_inference.py

python cnn_inference.py --model_path=models/ImageFolder/ImageFolder_resnet18_exp_1.onnx --class_path=models/ImageFolder/classes.txt --img_path=test1.jpg --image_size=224 --use_transform=True

'''
 Args:
'''
   --model_path :  ONNX model path
   --class_path : Class file (classes.txt) path contain class names
   --img_path  : Input image path
   --image_size : input image size
   --show_image : Display the image
   --use_transform : Use image transforms in pre-processing step (During the training, process images are Normalize with a mean and standard deviation)

Calculate Test Accuracy

Use the test_accuracy.py to calculate the ONNX model accuracy on the test data.

python test_accuracy.py --model_path=models/ImageFolder/ImageFolder_resnet18_exp_1.onnx --class_path=models/ImageFolder/classes.txt --img_dir=Image_Folder/test --image_size=224 --use_transform=True

The following illustrates 3 classes of the test image folder

├── Image_Folder
     ├── test
        │───── class1
        │     ├── class1.0.jpg
        │     ├── class1.1.jpg
        │     ├── class1.2.jpg
        │     ├── .........
        │     └── class1.500.jpg
        │
        │───── class2
        │     ├── class2.0.jpg
        │     ├── class2.1.jpg
        │     ├── class2.2.jpg
        │     ├── .........
        │     └── class2.500.jpg
        │
        └───── class3
               ├── class3.0.jpg
               ├── class3.1.jpg
               ├── class3.2.jpg
               ├── .........
               └── class3.500.jpg

All the test results will save in the folder "test_results" folder for each test experiment.

'''
 Args:
'''
   --model_path :  ONNX model path
   --class_path : Class file (classes.txt) path contain class names
   --img_dir  : Test images folder path
   --image_size : input image size
   --use_transform : Use image transforms in pre-processing step (During the training, process images are Normalize with a mean and standard deviation)

LahiRumesh/simple_cnn