Hello! I would want to compute the training accuracy of my neural network, but I want it to be IOU. I found something on the internet but mostly are for binary classification only, can anyone help me?
by the way this is my script so far:
def display_segmentation():
model = ESNet(classes=6)
path = F"/content/drive/MyDrive/Thesis_QuilangCastillano/Models/ESNET_5250.37-checkpoint.pt"
checkpoint = torch.load(path)
model.load_state_dict(checkpoint['state_dict'])
total = 0
correct = 0
torch.set_printoptions(edgeitems=128)
with torch.no_grad():
model.eval()
for i in tqdm(range(1)):
net_out = model(img[100].view(-1, 3, 384, 512))
segmentation_label = torch.argmax(net_out.squeeze(), dim= 0)
segmented_image = decode_segmap(segmentation_label)
label = np.unique(segmentation_label)
groundtruth = np.unique(mask[100])
alpha = 0.3 # how much transparency to apply
beta = 1 - alpha # alpha + beta should equal 1
gamma = 0 # scalar added to each sum
image = np.array(img[100].view(384, 512, 3))
prediction = cv2.addWeighted(segmented_image, alpha, image, beta, gamma, image, dtype=cv2.CV_64F)
Sub = f"Prediction: {label}, Groundtruth: {groundtruth}"
plt.imshow(prediction)
plt.title(Sub)
plt.show()
display_segmentation()
this script outputs this one:
the groundtruth array contains the class number of what class should be present in an image, the prediction array contains the class number of what the neural net thinks the image is composed of