I am new to deep learning and I want to be able to evaluate the model which underwent training for certain epochs using F1 score. I believe it requires the calculation of precision and recall first.
The model trained is SSD-300-Tensorflow. Is there a code or something that can produce these results? I am not using sci-kit or anything as I am not sure if that is required to calculate the score but any guidance is appreciated.
There is a file for evaluation in folder tf_extended called metrics.py which has a code for precision and recall. After training my files, I have the checkpoints in logs folder. How can I calculate my metrics? I am using Google collab due to hardware limitations (GPU issues)
If you are using the Tensorflow Object Detection API, it provides a way for running model evaluation that can be configured for different metrics. A tutorial on how to do this is here.
The COCO evaluation metrics includes analogous measures of precision and recall for object detection use cases. A good overview of these metrics is here. The concepts of precision and recall need to be adapted somewhat for object detection scenarios because you have to define "how closely" a predicted bounding box needs to match the ground truth bounding box to be considered a true positive.
I am not certain what the analog for F1 score would be for object detection scenarios. Typically, I have seen models compared using mAP as the single evaluation metric.