Open BartvanMarrewijk opened 2 years ago
Hello @studentWUR,
First of all, thank you for using VISSL and raising your question :) (and sorry for the delay in my answer...)
It would seem from your description that the model is not correctly loaded and so the weights are random, hence the random accuracy at the end. Could you check the logs and grep the line "Extra layers not loaded from checkpoint"? It will indicate if the weights are not loaded.
Thank you, Quentin
Instructions To Reproduce the Issue:
I have trained a pretrained ImageNet resnet on a custom dataset with 12 classes. For training I used following yaml file: training_yaml_file In this yaml file I only changed the of the head layer:
Then I trained the model with following commands:
While training the one-top-accuracy for the 5 different heads ranges between 80 and 90% For running inference I used following code, slighty adapted from second part of Inference_tutorial
Problem
Every time I am loading the model the weights are different
reload again:
In addition, the loaded mode has the ResNext, but is a resnet. As a result of the changing weights, my output is quite random. Anybody a solution?
Environment:
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