CV-Starter: Image Classification Using CNN

git clone https://github.com/AIFep-FDU/CV-Starter.git

Environment Setup:

1. Python Environment: Ensure you have Python installed (preferably Python 3.x).
2. Dependencies: Install required dependencies using pip:

pip install torch torchvision pytorchsummary numpy matplotlib tqdm

3. Colab: no need to pip

Code Flow:

1. Model Definition:

• The code defines a Convolutional Neural Network (CNN) model for image classification.
• ResNet-18 architecture is used as an example, but you can analyze and discuss other CNN architectures as well.

2. Training and Testing:

• The code includes functions for training and testing the CNN model.
• It utilizes datasets provided by PyTorch and DataLoader for efficient data handling.

3. Data Prepare Augmentation:

• Create a dataset class to read VOC2007 annotations and get its sub-images for classification.
• You need to design data augmentation techniques to improve accuracy. This may include rotations, flips, or other transformations.

4. Optimizer and Learning Rate Scheduler:

• Stochastic Gradient Descent (SGD) optimizer with momentum is used.
• Learning rate scheduling is implemented using ReduceLROnPlateau scheduler.

5. Experimental Requirements:
   • Complete the model training and validation code.
   • Crop the image directly and design data augmentation (including normalization) techniques to enhance accuracy.
   • Analyze and discuss the design of other optimizers and learning rate schedules.
   • Analyze and discuss different CNN or other model (based on timm) architectures.

Filling in the Blanks:

1. Custom Dataset Class:

   • Complete the __getitem__ method to crop the image based on the bounding box and apply transformations.

2. Model Architecture:

   • Adjust the ResNet model architecture by modifying the BasicBlock and ResNet classes.
   • Consider using other network designs or directly using models from libraries like timm for comparison.

3. Optimizer and Scheduler Configuration:

   • Modify the optimizer and learning rate scheduler settings in the main function to find the optimal parameters for training.
   • Fill in the code for updating the learning rate scheduler after each epoch.

4. Normalization Values:

   • Adjust the normalization values in the train_transforms and test_transforms to better suit the VOC2007 dataset.
   • Obtain these values through statistical analysis or use predefined values from similar datasets.

5. Loss Function and Backpropagation:

   • In the model_training function, fill in the code for calculating the loss using a suitable loss function and performing backpropagation.

6. Model Output and Loss Calculation in Testing:

   • In the model_testing function, fill in the code for obtaining the model's output and calculating the loss during the testing phase.

7. Data Augmentation:

   • Design image augmentation techniques inside the train_transforms to improve model performance.
   • Consider including transformations like random cropping, flipping, rotation, and color jitter.

Running the Script:

• Execute the script main.py from the command line: python main.py.
• Adjust parameters and configurations as needed for experimentation.
• For colab, just click the "run" button.

Submission:

After training your model, you can execute the python infer.py script to generate predictions for the 'test_1k' dataset. Simply zip the output and submit it directly to the CodaLab Competition.

Submit the code along with an experimental report (one page, No involution) to the e-learning platform.

Note:

• Modify and adjust the arguments, configurations, and experimental setup as necessary.
• Provide detailed explanations in the experimental report, covering code implementation, experimental setup, results, analysis, and discussions based on the specified requirements.