Which one is which ? (True Positive, True Negative, False Positive, False Negative)

Question

I am running confusion matrix on my own custom model using Tensorflow Object Detection API. I am using Faster R-CNN Inception v2 pets. I get this output:

Processed 100 images
Processed 200 images
Processed 300 images
Processed 400 images
Processed 500 images
Processed 500 images

Confusion Matrix:
[[1281.  233.]
 [ 581.    0.]]

  category  precision_@0.5IOU  recall_@0.5IOU
0   person            0.68797        0.846103

From this matrix:

[[1281.  233.]
 [ 581.    0.]]

Which one is true positive, true negative, false positive, false negative?

I am using code from this github . It said that this link would provide more explanation about this code, but the post went missing. So, i am confused.

Also, can i calculate accuracy from this results? Sorry if i'm wrong.

Answer 1

Please check below image.

More information about confusion matrix can be found here. https://www.analyticsvidhya.com/blog/2020/04/confusion-matrix-machine-learning/

Answer 2

True positive: 1281 True negative: 0. False Negative: 581. False Positive: 233.

Confussion matrix is a performance measurement for machine learning classification problem where output can be two or more classes or simplify we can assume CM calculate accuracy/loss your model.