Yes, you can use a base model trained on ImageNet, which typically involves images of size 256x256, for an object classification task on images of a different size, such as 320x360. However, there are some considerations and steps you should take to adapt the pretrained model to the new image size:
Resize Images: First, you should resize your input images from 320x360 to 256x256 or another size that is compatible with the pretrained model. Most pretrained models have a fixed input size, so you need to make sure your input images match that size. You can use interpolation methods like bilinear or bicubic resizing to preserve image quality.
Data Preprocessing: Perform the same preprocessing steps on your resized images as were done during training on ImageNet. This typically includes mean subtraction (subtracting the mean pixel value of the ImageNet dataset) and standardization (scaling pixel values to have a standard deviation of 1).
Model Architecture: Ensure that the architecture of the pretrained model is compatible with your classification task. The final fully connected layer of the pretrained model, which has the same number of output units as the number of classes in ImageNet (usually 1,000), may need to be replaced or fine-tuned for your specific classification task. You can replace it with a new fully connected layer with the appropriate number of output units for your task.
Fine-Tuning: Fine-tune the adapted model on your dataset. This involves training the model on your task-specific data while keeping the pretrained weights fixed (or optionally allowing some layers to be fine-tuned). Fine-tuning helps the model learn to recognize objects specific to your dataset while retaining knowledge from ImageNet.
Data Augmentation: Consider applying data augmentation techniques to increase the diversity of your training data and improve model generalization. Data augmentation can include random rotations, flips, translations, and changes in brightness and contrast.
Adjusting Input Size: If you want to use the larger image size (320x360) as your input, you can modify the architecture of the pretrained model to accept this input size. You may need to adjust the size of the input layer and potentially adapt other layers accordingly. Keep in mind that using larger input sizes may require more computational resources.
Evaluate and Fine-Tune: After training, evaluate the model on your validation set and fine-tune further if needed. Adjust hyperparameters like learning rate, batch size, and regularization techniques to optimize performance.
Testing: Finally, test the adapted model on your test dataset to assess its performance.
By following these steps, you can adapt a pretrained model to perform object classification on images of a different size while leveraging the knowledge and features learned from ImageNet. This transfer learning approach can save training time and potentially lead to better results than training a model from scratch.