hsiaohan0827/cnn-for-image-recognition

implement a simple CNN model using pytorch

CNN-for-image-recognition

implement a simple CNN model using pytorch

Setup

1. 創建一個新環境

python3 -m venv env_name

2. activate environment

source env_name/bin/activate

3. 安裝requirement.txt中的套件

pip3 install -r requirements.txt

Download Data

1. Using Medical Masks Dataset (https://www.kaggle.com/vtech6/medical-masks-dataset), comes from Eden Social Welfare Foundation which contains the pictures of people wearing medical masks along with the labels containing their descriptions

2. Download images and labels, transforming .xml to a .csv file, with header row 'filename', 'label', 'xmax', 'xmin', 'ymax', 'ymin'. For example:

   filename	label	xmax	xmin	ymax	ymin
   c1_1844849.jpg	good	1246	127	1312	227
   c1_1844849.jpg	none	745	889	862	999

3. Split data for train and test, name the file as train.csv / test.csv.

Training

1. run preprocess.py for both train.csv and test.csv.
   (remember to edit different output filename: croppedImg_xxxx / imgLabel_xxxx)

python3 preprocess.py

2. 修改cnn.py中的config

# CONFIG
output_dir = 'AlexNet_is32_bs256_ep300_loss'
if not os.path.isdir('CNN_model/'+output_dir):
    os.mkdir('CNN_model/'+output_dir)
logger = SummaryWriter('CNN_log/'+output_dir)

epochs = 500
bch_size = 256
lr = 0.001
imgSize = 32
save_freq = 50
istrain = True
modelPath = 'AlexNet_is32_bs256_ep300_loss+w/ep250.pkl'

configuration

• output_dir - model name.
• epochs - epoch number.
• bch_size - batch size.
• lr - learning rate.
• imgSize - input img size.
• save_freq - save modal every save_freq epochs
• istrain - if the model do train or test
• modelPath - model path for testing, work if istrain=False

3. run cnn.py

python3 cnn.py

tensorboardX

可以使用tensorboard觀察loss及accuracy變化

tensorboard --logdir CNN_log

Testing

1. 修改istrain及modelPath

istrain = False
modelPath = 'AlexNet_is32_bs256_ep300_loss/ep250.pkl'

2. run cnn.py, get classification result for each class.

python3 cnn.py

3. 可以在main中更換不同optimizer

if __name__ == '__main__':

    ......

    if istrain:
        # create model & optimizer
        cnn = CNN()
        cnn.apply(initialize_parameters)
        print(cnn) 
        
        # CHOOSE AN OPTIMIZER
        optimizer = torch.optim.Adam(cnn.parameters(),lr=lr, betas=(0.9, 0.999), eps=1e-08)
        #optimizer = torch.optim.SGD(cnn.parameters(),lr=lr)
        #optimizer = torch.optim.SGD(cnn.parameters(),lr=lr,momentum=0.8)        
        #optimizer = torch.optim.RMSprop(cnn.parameters(),lr=lr,alpha=0.9)

    ......