Training on new data with my own pretrained model - model fine tuning

❓ Questions and Help

I have trained a model on my own dataset (4 classes + background) starting from catalog://ImageNetPretrained/MSRA/R-50.

Now I want to use the output model from the training mentioned above model_final.pth as the new starting point (instead of R-50) to train on new labeled data. I set the MODEL.WEIGHT in my config to the path of “model_final.pth” and it is able to load the model and it has the right number of layers and dimensions. However, when running training, specifically model.train() inside engine/trainer.py function do_train(…), it skips all training and immediately finishes. As shown here:

2019-01-23 00:13:49,014 maskrcnn_benchmark.trainer INFO: Start training
2019-01-23 00:13:49,082 maskrcnn_benchmark.trainer INFO: Total training time: 0:00:00.067745 (0.0001 s / it)

Am I missing a step? If I am fine tuning with the same number of classes, I don’t think I need to remove any layers (e.g. cls_score), even when I remove those the model does not train.

Thanks for the help in advanced!

[fmassa]

Hi @adrifloresm ,

In order to do that, all you need to do is to open a python interpreter, load the weights and save a new dict with only the state_dict in it.

Something like

import torch

original = torch.load('path/to/your/checkpoint.pth')

new = {"model": original["model"]}
torch.save(new, 'path/to/new/checkpoint.pth')

Let me know if it solves your problem.

[fmassa]

A tutorial on how to finetune a model for a new dataset would be great!

I’d like to hold on a bit on adding yet more options in the config for now. I think that once we have the tutorial we would be able to see what are the biggest pain points, and have a better idea on how to address them.