Sure!
Based on your loss curves it looks like some hyperparameters might be a bit off (e.g. the learning rate might be too high). One approach would be to use your whole data and the model as it is now and to play around with the hyperparameters.
However, it might also be useful to check for some code bugs (e.g. forgetting to zero out the gradients etc.).
An easy way to check for code sanity is to use a very small subset of your data, e.g. just 10 samples (10 image - target pairs), and try to train your model on this small dataset. If your model can successfully overfit the data, it’s a good sign, as you’ll see that the code seems to work and the model architecture might be alright to learn the data distribution.
On the other hand, if your model can’t learn even these 10 samples, you should check for code bugs (or post your code in this forum so that we can help you debugging). 
Let me know, if this makes it clearer.