Model_classifier vs model_classifier_only

[Arthur023]

In the code where he tests the model we make 2 models. The model_classifier and model_classifier_only.

Now the weights are loaded into the model_classifier so I would assume that this model knows how to recognize the images. But in a later stadium when we want to look at the images and predict if something is visible on the picture we use the model_classifier_only. Which is the one without the loaded model.

I really don't understand why this is happening. And when I'm using the model_classifier to look at the image the model just gives a 50/50 chance to background or my class. (I only have one class beside background) So I would assume that the model_classifier is not trained. Which I just really can't imagine since it's the model we loaded the weights in.

If someone has any idea. Please let me know. Thanks in advance

[PeymanBaghdadi]

Hey @Arthur023 I have problem in directories on google colab, can you help me, maybe I pass my problem, I can help you. this is my issue: https://github.com/RockyXu66/Faster_RCNN_for_Open_Images_Dataset_Keras/issues/55