I am using Transfer Learning for Classification of my Dataset.

How to calculate Classification accuracy of each class?

I am using Transfer Learning for Classification of my Dataset.

How to calculate Classification accuracy of each class?

1 Like

I would just have an array `correct`

filled with zeros and size of number of total classes. Then I would classify data point `j`

, if it matches the target `label[j]`

then just increment the array with that class index, `correct[j] += 1`

. If `num_class`

is an array that contains the number of points for each class `c`

, then the accuarcy is `correct[c] / num_class[c]`

.

Answer given by @ptrblck Thanks a lot!

```
nb_classes = 9
confusion_matrix = torch.zeros(nb_classes, nb_classes)
with torch.no_grad():
for i, (inputs, classes) in enumerate(dataloaders['val']):
inputs = inputs.to(device)
classes = classes.to(device)
outputs = model_ft(inputs)
_, preds = torch.max(outputs, 1)
for t, p in zip(classes.view(-1), preds.view(-1)):
confusion_matrix[t.long(), p.long()] += 1
print(confusion_matrix)
```

To get the per-class accuracy:

```
print(confusion_matrix.diag()/confusion_matrix.sum(1))
```

11 Likes

how to calculate overall precision and recall here

Yes, you should calculate the accuracy on your test images. I also suggest you create stratified 5 or 10 fold experiment.

Thank you so much Mona for your reply.

This doesnâ€™t calculate accuracy. The true negatives are missing from the numerator of your fraction:

```
confusion_matrix.diag()/confusion_matrix.sum(1)
```

You are either calculating precision or recall. I donâ€™t know which of the two, because I canâ€™t tell which of the axis is prediction and which is ground truth.

Really nice! Thanksâ€¦