General steps for fine-tuning a pre-trained model

NOTE: Other CNNs models such as ResNet or mobilenet are applicable as well. You can find these modelshere.

1. Label images:

• For image classification tasks, the labeling process involves assigning a class label to each image and organizing them into folders based on their classes. Tools like RoboFlow or CVAT simplify this process.

2. Split Data:

• Divide dataset into training and validation sets. The training set is used to train the model, while the validation set is used to evaluate its performance during training.


3. Pre-process the images: (Resize images and batching dataset)

• Resize images into appropriate sizes, each pre-trained model has a different image size. For EfficientNet, the image size used was 128x128 for the smallest model.
• Batch size for image classification can vary, a good starting point is 32 for batch size.

4. Augment Images (if needed):

• Data augmentation techniques can be applied to artificially increase the diversity
of your dataset. This helps the model generalize better.


5. Train the model:

• The models were trained using PyTorch, feel free to use tensorflow if you want. Monitor metrics such as train loss and val loss during training.


6. Validate the Model:

• Evaluate the trained model on the validation set to ensure it generalizes well
to new, unseen data.


7. Inference on Test Set:

• After training, perform inference on a separate test set to assess the model's performance on completely unseen data.