Low validation loss from the first epoch?

Question

The initial validation loss is low from the first epoch and then decreases slightly. What does this actually mean? Does it indicate that the model can effectively and quickly identify patterns for this task?

I can see that the model works in practice, but the results (some image restoration) aren’t ideal yet, so I want to improve its performance even further.

1. Given this low loss from the first epoch, should I focus on training with more data or on adjusting the architecture and layers to be even more complex, etc.?

2. Given the small differences between the first epoch and the last, is it more likely that the model was barely able to improve performance during these epochs, or could the difference in loss still be meaningful?

The dataset count was of 10,000 images - 0.9 for training, 0.1 for validation.

First epoch loss: Epoch [1/50], Training Loss: 0.026428, Validation Loss: 0.023727 Last epoch and plateau: Epoch [34/50], Training Loss: 0.020682, Validation Loss: 0.020651

Answer 1

The intilal low validation loss from your first epoch suggests a few key points about your model:

Initial Low Loss:

The model is likely learning useful patterns quickly from the dataset, so you see a low loss from the first epoch. This could mean that the task is not overly complex, or the model architecture is well-suited for initial learning. However, it might also indicate that the model is not learning more fine-grained details, especially if the task involves image restoration, which can be challenging.

Plateau in Loss:

The loss plateaus around epoch 34 with little further improvement suggests that the model might have reached a point of diminishing returns. This means that either the model has learned everything it can from the given data and architecture, or it is struggling to capture more intricate details or patterns.

to slove this issue you can try following things

1. Training with more data

=> If your dataset is not large or diverse enough, the model might have learned the patterns quickly but lacks enough examples to generalize well. More data, especially if it covers a wider range of scenarios (like different image qualities or variations), can help the model improve its performance. you should the augmentation of your dataset since you have mentioned about the 10,000 images but if they are mostly same it would be not a good dataset here in dataset the quality of the data that matters instead of the quantity of the dataset

2. Adjusting the Architecture:

=> If the model learns quickly but struggles to refine its output or learn more detailed patterns, the architecture might not be complex enough for your task. This is often the case for tasks like image restoration, where deep networks with residual connections, multi-scale architectures, or specialized layers (like attention mechanisms) may help capture finer details. if you want to try unet with attention you can follow this article