Inference problem on the new trained weights about rngdetplusplus HOT 4 OPEN

Sorry that RNGDet/RNGDet++ is trained on conventional supervised learning, which does not consider pretrain-finetune pipeline. The provided checkpoints are not trained on enough data, so directly finetuning the checkpoints may not produce satisfactory results. I think you might need to enlarge your dataset and train the network from scratch instead of finetuning on a small amount of data.

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In my idea, this problem is in the main_training.py or agent.py codes as each epoch calculated zero extracted_candidate_initial_vertices which are odd!!. Therefore, I am looking into the problem of not finding any vertices in each epoch. even though your existing pre-trained checkpoints are able to extract initial_vertices in each image.

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Sorry for the late reply.

You may want to check the predicted segmentation map to generate the candidate initial vertices, which is visualized here.

If no candidate initial vertices are predicted, it means the segmentation map is all zero. Since you use data from your own datasets, this may cauzed by different properties of different datasets. You may want to alter some parameters to make the segmentation network work properly.

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Thanks for your reply. I figured out that the problem was because of the low number of images in the dataset, and the model couldn't generate the right checkpoints for them. I thought, for the fine-tuning, the number and volume of the dataset didn't matter so much. but maybe I was wrong.
could you please give me a hint on how I can finetune the model and the pretrained checkpoints that you already Gave access to everyone?
In addition, do you have any idea that at least how many numbers of images are needed for finetuning?

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