Hi. I am trying to compare the original image, the ground truth mask and the predicted mask by displaying them. However, I am entirely new to this and I would like to ask for anyone’s opinion if am I doing it right. Here is my prediction script:
def test(model):
  model.eval
  correct = 0
  total = 0

  with torch.no_grad():
      for i in tqdm(range(len(img_test))):

          
          real_class = mask_test[i]
          net_out = model(img_test[i].view(-1, 3, 448, 448))[0]
          _,predicted_class = torch.max(net_out, 0)
          
          prediction = predicted_class.eq(mask_test[i])

          
          plt.figure(figsize=(22,8))
          plt.subplot(1, 3, 1)
          plt.imshow(real_class.detach().cpu().squeeze())
          plt.title('original mask')

          plt.subplot(1, 3, 2)
          plt.imshow(prediction.detach().cpu().squeeze())
          plt.title('predicted mask')

          plt.subplot(1, 3, 3)
          plt.imshow(img_test[i].view(-1, 448, 448, 3).detach().cpu().squeeze())
          plt.title('original image')

          break
          

      
test(model)

I am asking this because the ground truth and my predicted mask is almost identical, I fear it was just inverting colors.